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Population Health Needs Assessment

Oklahoma State Innovation Model

7/30/2015

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OKLAHOMA STATE DEPARTMENT OF HEALTH

July 30, 2015

PopulationHealth Needs Assessment Draft Version 3

1 Population Health Needs Assessment

Oklahoma State Innovation Model

Contents 1. KEY FINDINGS ...... 7 A. KEY HEALTH METRICS ...... 8 2. INTRODUCTION ...... 9 A. NEEDS ASSESSMENT DESIGN ...... 9 METHOD OF NEEDS ASSESSMENT ...... 9 3. OKLAHOMA OVERALL HEALTH...... 10 A. POPULATION HEALTH ...... 12 BLEND OF RURAL AND URBAN ...... 12 EMPLOYMENT, INCOME, AND POVERTY ...... 13 FOOD INSECURITY ...... 14 EDUCATION ...... 14 HEALTH LITERACY ...... 15 OKLAHOMANS WITH DISABILITIES ...... 15 HOUSING ...... 16 COMMUNITY CONCERNS ...... 16 B. CLINICAL CARE ...... 20 LACK OF HEALTH INSURANCE ...... 20 HEALTH PROVIDER ACCESS ...... 20 HEALTH SYSTEM PERFORMANCE ...... 22 C. HEALTH COSTS ...... 24 4. TOBACCO USE ...... 27

A. POPULATION HEALTH ...... 28

B. CLINICAL CARE ...... 29 POPULATION-BASED CESSATION SERVICE...... 29 C. HEALTH COSTS ...... 30

5. OBESITY ...... 32 7/30/2015

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A. POPULATION HEALTH ...... 33 PHYSICAL ACTIVITY...... 34 NUTRITION ...... 35 B. CLINICAL CARE ...... 39 C. HEALTH CARE COST ...... 40 6. DIABETES ...... 41 A. POPULATION HEALTH ...... 42 B. CLINICAL HEALTH ...... 44 C. HEALTH CARE COSTS ...... 45 PREVENTABLE HOSPITALIZATIONS ...... 46

7. HYPERTENSION...... 47 PopulationHealth Needs Assessment

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A. POPULATION HEALTH ...... 48 B. CLINICAL CARE ...... 49 C. HEALTH CARE COSTS ...... 50 PREVENTABLE HOSPITALIZATIONS ...... 50 8. BEHAVIORAL HEALTH ...... 51 A. POPULATION HEALTH ...... 52 B. CLINICAL CARE ...... 54 C. HEALTH COSTS ...... 56 9. CHILDREN’S HEALTH ...... 57 A. POPULATION HEALTH ...... 58 B. CLINICAL CARE ...... 62 C. HEALTH COSTS ...... 65 10. APPENDICES ...... 66 A. COMPARISON OF RANKING INDICATORS...... 67 B. OKLAHOMA REPORT, AMERICA’S HEALTH RANKINGS®, 2014 ...... 70 C. OKLAHOMA AND U.S. MEASURES, COUNTY HEALTH RANKINGS DATA ...... 71 D. COUNTY LEVEL RISK FACTORS AND CHRONIC DISEASES AMONG PERSONS 18 YEARS AND OLDER, OKLAHOMA BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, 2013 ...... 74 E. COUNTY LEVEL MORTALITY BY CAUSE AND YEARS, OKLAHOMA VITAL STATISTICS ...... 78 F. DATA SOURCES AND METHODS ...... 83 LIMITATIONS ...... 84 11. ENDNOTES ...... 86

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PopulationHealth Needs Assessment

3 Oklahoma Health Improvement Plan (OHIP) Health Efficiency and Effectiveness Workgroup July 30, 2015

Overarching Goal Create a system of outcome-driven healthcare that supports patients and healthcare providers in making decisions that promote health by emphasizing preventive and primary care and the appropriate use of acute care facilities. Objective 1 Reduce by 20% the rate, per 100,000 Oklahomans, of potentially preventable hospitalizations from 1656 in 2013 to 1324.8 by 2020. Objective 2 Reduce by 20% the rate, per 1,000 population, of Hospital Emergency Room Visits, from 500 in 2012 to 400 Visits by 2020.

Workgroup Members Becky Pasternik-Ikard, Deputy State Medicaid Director, Oklahoma Health Care Authority and Workgroup Chairperson Lisa Anderson, Assistant Vice President of Care Coordination, INTEGRIS Health

Mary Brinkley, Executive Director, LeadingAge Oklahoma

Michael Brose, Executive Director, Mental Health Association Oklahoma

Joni Bruce, Executive Director, Oklahoma Family Network

Steven Buck, Deputy Commissioner, OK Dept. of Mental Health & Substance Abuse Services

Lou Carmichael, Executive Director, Variety Care, Inc.

Steven Crawford, Legislative Co-Chair, Oklahoma Academy of Family Physicians

Claire Dowers-Nichols, Associate Director, Oklahoma Healthy Aging Initiative

Donna Dyer, Chief Executive Officer, East Central Oklahoma Family Health Center, Inc.

Wanda Felty, Community Leadership & Advocacy Coordinator, Oklahoma LEND

7/30/2015 | | LaWanna Halstead, Vice President of Clinical & Quality Initiatives, Oklahoma Hospital Association

Priscilla Haynes, Community Health Division Chief, Tulsa City-County Health Department

Karen Hylton, Programs Manager, OK Department of Human Services

Teresa Jackson, Senior Executive Officer for Health, Choctaw Nation of Oklahoma

Jon Lowry, Chronic Disease Service Director, Oklahoma State Department of Health

Lynn Mitchell, Chief Medical Officer, OU Health Science Center PopulationHealth Needs Assessment

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Marisa New, Health Equity and Resource Opportunities Manager, Oklahoma State Department of Health

Ann Paul, ACO President, Vice President of Population Health Initiatives, St. John Health System

Lisa Pivec, Community Health Promotion Director, Cherokee Nation

Dan Straughan, Executive Director, Homeless Alliance

J. David Thompson, Senior Vice President, Chief Operating Officer, GlobalHealth, Inc.

Marla Throckmorton, Clinic Administrator, Absentee Shawnee Tribe of Oklahoma

Workgroup Project Manager, Valorie Owens, Center for Health Improvement and Innovation, Oklahoma State Department of Health

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PopulationHealth Needs Assessment

5 Authors and Contributors

Oklahoma State Innovation Model (OSIM) Evaluation and Performance Reporting Committee

Malinda Reddish Douglas, DrPH, CPH Sr. Chronic Disease Epidemiologist, Oklahoma State Department of Health and Subcommittee Chairperson

Becky Pasternik-Ikard, JD, RN, MSN Deputy State Medicaid Director, Oklahoma Health Care Authority Derek Pate, DrPH Health Care Information Manager, Oklahoma State Department of Health Miriam McGaugh, PhD Community Epidemiology and Evaluation Manager, Oklahoma State Department of Health Pam Archer, MPH Office of Scientific and Research Integrity Director,, Oklahoma State Department of Health Rebecca Moore, MA Informatics Manager, Oklahoma State Department of Health Ryan Morlock, MS Statistician, Oklahoma Health Care Authority Valorie Owens, MSW Statewide Access to Care Planning Manager, Oklahoma State Department of Health Subcommittee Staff James Rose, MA Statewide Access to Care Planning Statistician, Oklahoma State Department of Health Subcommittee Staff

University of Oklahoma, E-TEAM Adam Gibson, PhD

Senior Research Associate, Project Lead 7/30/2015

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Geneva Strech, MEd, MHR Associate Director

Carol Surber, MA Senior Research Associate, Data Analyst

John Vetter, MA Senior Research Associate, Technical Services

PopulationHealth Needs Assessment

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1. Key Findings All groups experience adverse health outcomes due to chronic disease and health risk behaviors. Individuals from all geographies, race, gender, and age groups experience poor health outcomes. Evidence from high rates of chronic disease patterns, hospitalizations due to preventable causes, and patterns of unhealthy behaviors compels those seeking to improve health to consider interventions at the structural, policy, and community-wide level in order to positively impact the long-term health of as many Oklahoma residents as possible. A societal shift toward healthier lifestyles that includes quality nutrition, daily physical activity, optimal weight control, social support, tobacco cessation, and reduced substance abuse can have profound positive impacts on Oklahoma’s health. Special consideration for mental health, a chronic condition that significantly influences overall health, is critical for achieving population health goals.

Greater socio-economic need and health impacts are found among certain groups and places. There are areas of high socio-economic need across all counties, especially in southeastern Oklahoma. Disparities in educational attainment are also found across Oklahoma. These areas and groups with high socio-economic need are also the most affected by health problems, as evidenced by significantly worse health outcome measures, higher hospitalization rates, and myriad health challenges. While Oklahoma has relatively good health insurance coverage, some lower resourced Oklahomans remain uninsured. Oklahoma residents with a disability are also more likely to live in poverty than the general population, which puts them at further disadvantage to accessing needed care and services. When planning for heath improvement, careful consideration should be given to the highest need groups identified geographically by socio-economic measures.

Limited care access results in greater health impacts. Access to health care is challenging in many counties due to shortages of primary and specialty care. Access challenges also exist for those with no or limited insurance, cultural differences, or complicated needs. Federally designated underserved areas and populations cover nearly the entirety of Oklahoma. Unmet behavioral health, chronic disease management needs, health education and literacy needs, economic development, and healthy behavior supports are recurring themes supported by data and key informant interviews. Addressing the medical and mental health shortage areas and increasing access to medical and community care are important needs in Oklahoma.

Rates of preventable hospitalizations are indicators of population‐level access to primary care or

community care. For chronic diseases, some hospitalizations could be avoided with regular and appropriate care that prevents the conditions from worsening. The cost associated with the hospital admissions that could have been

avoided through high‐quality outpatient care is high. More than $14 million dollars in health care costs could be saved 7/30/2015

| | with a 20% drop in the nearly 6,280 preventable hypertension- and diabetes-related hospitalizations that occurred in

Oklahoma during 2013. Specifically, the estimated cost savings from preventing 20% of the preventable hospitalizations associated with hypertension was $1,822,857 and complications from diabetes was $12,200,776.

Diabetes, hypertension, obesity, physical activity and nutrition, and tobacco use are risk factors associated with heart disease and cancer, the leading causes of death in Oklahoma. Many risk factors have been identified as increasing the risk for chronic disease. Some of these risk factors can be modified, changed, to decrease the risk of chronic disease or the complications associated with chronic disease. Tobacco use/exposure, physical inactivity, poor nutrition, and obesity are common risk factors for many chronic diseases and associated complications. Intervention strategies focused on common risk factors can prevent or delay multiple chronic diseases,

but must be implemented with a long-term perspective and sustained effort. PopulationHealth Needs Assessment

7 A. Key Health Metrics METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET Overall Health Ranking America’s Health 46 n.a. Ranking® (2014) Commonwealth Fund 49, 4th quartile n.a. 3rd quartile State Health System (2014) Performance Scorecard Tobacco Use Adult Smoking 23.7% 18.8% 18.0% Prevalence (2013) (2013) 15.1% High School 12.7% High School 10.0% Youth Smoking (2013, unweighted) (2013) Prevalence 4.8% Middle School 2.9% Middle School 2.0% (2013) (2013) Obesity 32.5% 29.4% 29.5% Adult Obesity (2013) (2013) 11.8% High School 13.7% High School 10.6% Youth Obesity (2013) (2013) Adult No Leisure Time 33.0% 26.3% 20.8% Activity (2013) (2013) 49.6% at least 1/day 60.8% at least 1/day 50.0% at least 1/day Adult Fruit Consumption (2013) (2013) Adult Vegetable 1.5/day 1.6/day 2.1 per day Consumption (2013) (2013) Food Desert/Food 21.1% of population 12.3% of population Under development Availability (2013) (2013) Diabetes 11.0% 8.7% 9% Adult Diabetes (2013) (2010) Hypertension 37.5% 31.4% 36%

Adult Hypertension (2013) (2013) 9,703 n.a. 8,204 Heart Disease Deaths (2013)

Behavioral Health 7/30/2015

86% treatment gap n.a. 76% gap | Untreated Mental Illness (2012-13) 8.81% 8.66% 7.8% Addiction Disorders (2012-13) (2013) Suicide Deaths 22.0 per 100,000 16.5 per 100,000 19.4 per 100,000 18 years and older (2013) (2013) Children’s Health 6.8 per 1,000 live births 6.0 per 1000 live 6.4 per 1,000 live births Infant Mortality (2013) births (2013) 29.9 per 100,000 live births 17.8 per 100,000 live 26.2 per 100,000 live births Maternal Mortality (2013) births (2011) Injury Deaths Among 0-17 14.4 per 100,000 7.4 per 100,000 13.9 per 100,000

years (2013) (2013) PopulationHealth Needs Assessment

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2. Introduction Oklahomans face many serious challenges to the quality of their health. The state of Oklahoma is dedicated to creating a healthcare system that provides high quality and affordable health care to its residents. The state appreciates the opportunity afforded by the federal State Innovation Model (SIM) cooperative agreement to help move toward a health care system that is innovative and capable of delivering quality health care at a better value for both the state and its residents.

The purpose of this comprehensive needs assessment is to offer a meaningful understanding of the health needs of Oklahomans and to serve as the foundation for setting statewide health priorities to help with the development of the Oklahoma SIM. The goal of the SIM is to provide state-based population and clinical interventions to improve health, provide better care, and reduce health expenditures for Oklahomans.

A. Needs Assessment Design This assessment was designed to answer the questions of the state’s needs: 1) to improve population health; 2) to have better clinical care; and 3) to reduce healthcare costs. The specific areas of health improvement that govern the assessment are those specified in Oklahoma’s response to the SIM cooperative agreement and the Oklahoma Health Improvement Plan.

 Tobacco use  Hypertension  Obesity  Behavioral health  Diabetes  Children’s health, including maternal health

Special attention has been given to the issues of tobacco use, obesity, diabetes, hypertension, and behavioral health as these are the major areas of concern with respect to the health of Oklahomans, the cost to the healthcare system, taxpayers, and businesses, and the linkage with many other health issues, such as coronary heart disease, lung disease, cancer, arthritis, infertility, kidney disease, eye disease, neuropathy, immunosuppression, and suicide.

Method of Needs Assessment The archival method was used to conduct the needs assessment. This method uses data that already existed in routinely maintained databases and records. Such data are typically proven as valid and specific and allow for comparison between state and federal level indicators. On the other hand, archival data may not exactly match the intent of the needs in question. Wherever possible, additional qualitative and quantitative data were considered to augment the analysis. A need was defined as a measurable discrepancy or gap between what is and what should be.

7/30/2015 Expressed as an equation, it is: Target – actual = need

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Data Sources and Methods An extensive array of secondary data was collected and synthesized for this report. Secondary data was analyzed using OK2SHARE, a publicly available data platform with a dashboard with multiple variables from 14 state and nationally reportable data sources that allowed for Oklahoma-specific race, age, gender, and geographic details. Online data queries were also run through the Centers for Disease Control and Prevention Wideranging Online Data for Epidemiologic Research (WONDER), Behavioral Risk Factor Surveillance, and Youth Risk Behavior Survey. Additionally, this extensive core data was analyzed using a highly systematic and quantitative approach that incorporated multiple benchmarks and comparisons of priorities identified by the Oklahoma Health Improvement Plan, Healthy Oklahomans 2020 and the 2014 State of the State’s Health Report. And, lastly, supplemental information from recently published reports on Oklahoma’s health and access to care were reviewed for additional

PopulationHealth Needs Assessment key information on important topics such as health disparities as well as primary care and behavioral health needs.

9 3. Oklahoma Overall Health Oklahoma is 68,595 square miles of diverse ecosystems that include plains, prairie, mountains, lakes, forests, swamps, and sand dunes.1 The people of Oklahoma are just as diverse as its landscape. The culture of Oklahoma has many regional influences on its cultural norms, philosophies, and vernacular. The cultural identities within areas of Oklahoma are: The West culture in panhandle counties; Middle West culture in north central counties, Western culture in western counties, Southern culture in central counties, Ozarks culture in northeastern counties, and Deep South culture in southeastern counties.2 Yet, as a whole, the state continues to rank poorly for both population health and health systems.

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Overall Health Ranking

America’s Health 46 NA Ranking® (2014) Commonwealth Fund 49, 4th quartile NA 3rd quartile State Health System (2014) Performance Scorecard

America’s Health Rankings®, a project of the United Health Foundation, is the longest-running all state analysis of health and the factors that affect it. America’s Health Rankings encompass a comprehensive set of clinical care, public policy, social determinants (community and environment), and behavioral measures of health indicators and outcomes for use in health improvement planning. Its purpose is to stimulate action by public officials, health care professionals, public health professionals, employers, educators, and communities to improve the health of the states and nation.

The State Health System Performance Scorecard is a project of the Commonwealth Fund, whose mission is “to promote a high-performing health care system that achieves better access, improved quality, and greater efficiency, particularly for society’s most vulnerable, including low-income people, the uninsured, minority Americans, young children, and elderly adults. In 2009 and 2014, the Scorecard ranked states based upon 42 indicators of health care access, quality, costs, and outcomes. The scorecard series provide performance benchmarks and improvement targets

for states and the nation. 7/30/2015

See Appendix of a comparison of indicators of the various ranking systems. |

TRENDS IN HEALTH METRICS. ®  America’s Health Ranking 50 50 49 population health shows 48 Oklahoma’s rankings from 46 46 46 46 46 2010 to 2014. 44 44  Health System 42 Performance Scorecard shows the state’s rankings 40 America's Health Ranking® Health Systems Scorecard from 2009 and 2014.

2009 2010 2011 2012 2013 2014 PopulationHealth Needs Assessment

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Healthy Oklahoma 2020 – Oklahoma Health Improvement Plan

Oklahoma is not keeping up with the rest of the nation, which means more Oklahomans are dying unnecessarily each and every year.

Given Oklahoma’s rankings for health determinants, it is not unexpected that it has the 4th highest rate of deaths among all the states in the nation. The state mortality rate (941.9 per 100,000, age-adjusted) is 23% higher than the national rate. While Oklahoma’s mortality rate dropped 5% over the past 20 years, the national mortality rate dropped 20% during the same period. Many have died prematurely with nearly 343,750 years of life lost due to deaths before the age of 75 years. In 2013, 38,379 Oklahomans died from various causes.

LEADING CAUSES OF DEATH BY AGE GROUPS, OKLAHOMA, 2013. Causes of Death Overall Leading Cause 2nd Leading Cause 3rd Leading Cause 4th&5th Leading 2013 Rank & Cause # #1 45-54 years 35-44 years <1 year Heart Disease 9,703 65 years and older 55-64 years 15-24 years 25-34 years #2 55-64 years 5-14 years 35-44 years 15-24 years Cancer 8,029 45-54 years 25-34 years 65 years and older Chronic Lower #3 65 years and older 55 -64 years Respiratory 2,680 Disease #4 1-4 years <1 years 55-64 2,454 5-14 years 45-54 years Unintentional 15-24 years 55-64 years Injury 25-34 years 35-44 years #5 65 years and older Stroke 1,878 #6 55-64 years Diabetes 1,268 Alzheimer’s #7 65 years and older Disease 1,145 Influenza/ #8 <1 year Pneumonia 758 #9 15-24 years 45-54 years Suicide

7/30/2015 664 25-34 years | | Nephritis/ #10 Nephrosis 606 While leading causes of deaths are exclusively categorized, many of the diseases and conditions are related.

High blood pressure, high cholesterol smoking, physical inactivity, obesity, poor diet, and diabetes are the leading causes of cardiovascular disease.3

Many of these same cause also raise the risk of cancer.4 PopulationHealth Needs Assessment

11 A. Population Health From 2010 to 2014, the total population of Oklahoma grew from 3,761,702 to an estimated 3,878,051 (3.1% increase).5 Latest estimates show that 80% of the population reported their race as white, followed by American Indian (13%); African-American (9%); some other race (3%); Asian (2%); and/or Native Hawaiian and other Pacific Islander (<1%).6 The population was distributed among 50.5% females and 49.5% males. One-quarter (24.5%) of the state’s residents were less than 18 years of age and 61% were between the ages of 18 and 64 years of age.

PERCENTAGE OF POPULATION AGED 65 YEARS AND OLDER, U.S. CENSUS ESTIMATES, OKLAHOMA.

40%  In 2013, an estimated 14% of Oklahoma’s 30% population were 65 years of age and older. 19%  By 2030, nearly 1 in 5 Oklahomans will be 65 20% 14% years of age and older.7 10%  This change will represents a 36% increase in the proportion of Oklahoma’s population that is 65 0% 2013 2030 years and older.

In 2014, Oklahoma was ranked 47th in the health of older adults, an improvement from a 49th ranking in 2013.8 America’s Health Ranking® – Senior Report listed the significant challenges for the health of older adults in Oklahoma as: hip fractures, physical inactivity, falls, overall unhealthy behaviors, and hospitalized older adults who received recommended care for heart attack, heart failure, pneumonia, and surgical procedures.

Blend of rural and urban In 2014, there were 54.7 persons per square mile in Oklahoma, ranging from 1.3 in Cimarron County to 1,058 in Tulsa County.9 Approximately one-third of Oklahomans lived in rural communities.10 On the other hand, 58.4% of Oklahomans lived within the 14 rural and urban counties that make up the Tulsa and Oklahoma City metropolitan statistical areas.11 The population in metropolitan areas has steadily increased since the middle of the last century with much of the population growth concentrated around the metropolitan areas and expanding suburban communities 12 while counties in western Oklahoma have decline in population.

POPULATION DENSITY, PERSONS PER SQUARE MILE BY COUNTY, OKLAHOMA, 2014.

 Counties in the 7/30/2015

green and brown |

colors indicate fewer persons per square mile.  Red colored counties indicate more persons per square mile.  Most densely populated counties in shaded in the darkest

red color. PopulationHealth Needs Assessment

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Employment, Income, and Poverty In 2014, Oklahoma’s unemployment rate of 4.5% was lower than the national rate of 5.6%. However, the unemployment rate varied across the state ranging from 2.5% in Grant County to 8.9% in McIntosh County. UNEMPLOYMENT RATES BY COUNTY, OKLAHOMA, SY2014.  Grant County’s unemployment rate was lower than the state’s historical low rate of 2.9%.  McIntosh County’s rate was the same as the state’s historical high rate of unemployment (8.9%).  Nearly one-third (31.1%) of Oklahoma’s counties had a higher rate of unemployment than the nation.  The unemployment rates in the eastern part of the state were higher than the rates in the western part of the state.

The Business Health and Wellness Survey13 project gathered Oklahoma employer perspectives on health insurance and wellness programs as they relate to workforce costs, productivity, and returning value on investment. Over half (55%) of the survey respondents stated they had difficulty finding employees to meet their needs and, of those, 58% were actively involved in promoting workforce readiness through activities such as providing internships and working with educational or training institutions to develop skilled workers. Additionally, half of the respondents indicated that employee health impacts their businesses. Almost 40% of survey respondents paid 100% of employee health plan premiums and less than 1% paid none. Of the remaining: 19% paid 26%-50%, 18% paid 51%-75%, and 17% paid 76%-99%. Moreover, the respondents said that rising health care costs were impacting the bottom line of businesses; yet, 83% said that offering coverage was the right thing to do. As one respondent put it, “People trying to put their families on

our insurance – it’s incredibly expensive for them. I can’t even imagine trying to pay those amounts.”

INCOME AND LACK OF INSURANCE, OKLAHOMA, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, 2013.  One-third of persons with 50% household incomes less than 40%

7/30/2015 33% 32% $25,000 did not have health | | 21% 30% insurance. 13% 20%  The lack of insurance was 6% 10% statistically different between 0% incomes under $25,000 and <$15,000 $15,000-24,999 $25,000-34,999 $35,000-49,999 $50,000+ each of the income categories over $24,999.

Overall, the state’s median household income of $45,339 was 14.5% lower than the national median of $53,046.14 Accordingly, more Oklahomans (16.8%) earned income in the past 12 months that was below poverty level, compared to the national average of 15.8% in 2013. Poverty is not evenly distributed with nearly one-quarter (24%)

of Oklahoma’s children (under 18 years) living in poverty.15 Many studies draw the link between poverty and health. PopulationHealth Needs Assessment

13 Food Insecurity In 2012, an estimated 17.2% (656,300) of Oklahomans, including nearly 239,380 children, experienced a lack of access to enough food for all household members and uncertain availability of nutritiously adequate foods.16 In more than half of Oklahoma’s counties (43/77, 55.9%), rural residents must travel more than 10 miles to reach a full service grocery store and urban residents must walk more than a mile to a grocery store.17

Diabetics who experience food insecurity (worry about having enough food to eat) have higher A1C (10) compared to diabetics without food insecurity (7.6).18

Education In 2013, 85.0% of Oklahomans 25 years and older had at least a high school degree, of which 32.5% attended college but obtained no degree. Less than half of Oklahomans over 25 years of age (46.9%) had no college experience.19 Overall, 7% of Oklahomans earned an associate’s degree, 16% earned a bachelor’s degree, and 8% earned a graduate or professional degree.20 One in four Oklahomans without a high school education lived in poverty, compared to one in 20 with a college degree.

According to the 2013 Behavioral Risk Factor Surveillance System, more than one-third (36%) of Oklahomans with less than a high school education were employed. Of the remaining, 11% were out of work, 23% were unable to work, 13% were homemakers, 13% were retired, and 3% were students.

DAILY LIVING DIFFICULTIES BY EDUCATIONAL STATUS, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.  Oklahomans with less than a high 3% Errands done alone 9% school education were more likely to

18% have difficulty with daily living activities 7% Concentrating/Remembering 14% than those with more education. 30% 2%  About one-third (34%) of those with Dressing/Bathing 5%

11% less than a high school education 7/30/2015 | | 10% Walking/Climbing stairs 19% reported difficulty walking or climbing 34% stairs and/or difficulty concentrating or 3% Blind/Seeing w/glasses 6% remembering. 15%  Adults with less than a high school 0% 10% 20% 30% 40% education were evenly distributed across College H.S. < H.S. all age categories: 15% were 18-24 years; 19% were 25-34 years; 16% were 35-44 years; 16% were 45-54 years; 14% were 55-64 years; and 20% were 65 years and older.

Overall, 45% (40.7-49.4%, 95% CI) of those with less than a high school education reported their general health as fair or poor. The percentage was significantly different than the 16.3% (15.3-17.3%, 95% CI) of those with at least a high school education who reported their general health as fair or poor. PopulationHealth Needs Assessment

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Health Literacy Health literacy is defined as, “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” 21 Adults who lack basic prose literacy skills (BPLS) may be able to locate easily identifiable information in short text that is organized in sentences or paragraphs and unable to read or understand any written information in English. Health literacy barriers can affect the ability to locate health care providers, complete medical forms, follow treatment, and take preventive actions. Research found that persons with limited health literacy skills were more likely to skip important preventive measures such as mammograms, Pap smears, and flu shots.22 Another study showed that patients with limited health literacy skills enter the healthcare system when they were sicker, as compared to patients with adequate health literacy.23 Moreover, patients with limited literacy skills have higher rates of hospitalization and use of emergency services. They are significantly more likely to report their health as poor.24

Two national studies conducted a decade apart found that 12-13% [1993: 12% (10.4-14.0% 95% CI), 2003: 13% (8.5-18.8% 95% CI)] of Oklahomans 16 years and older had BPLS. There was no significant change in the percentage of the population from 1993-2003.25 Interventions in the primary care and community settings are useful in supporting sustained change in health literacy for change in behavioral risk factors.26

Oklahomans with Disabilities Approximately 15.8% of Oklahomans living at home have a disability. 27 Among Oklahomans who are 65 years of age and older, 42.3% have a disability. While 14.1% of Oklahomans 18 to 64 years of age have a disability, they accounted for the largest number (319,463) of those with a disability.

PERCENTAGE OF POPULATION BY AGE GROUP WITH DISABILITY, U.S. CENSUS, OKLAHOMA, 2009-2013. Under 18 18 to 64 Years 65 Years and Years Older Population 4.8% 14.1% 42.3%

7/30/2015 with | |

(n=44,819) (n=319,463) (n=212,800) Disability

According to the Behavioral Risk Factor Surveillance System in 2013, those who have activities limited due to a physical, mental, or emotional problem were more likely to delay a visit to the doctor because of the cost (29.6%, 27.1-32.2%, 95% CI) than those without limited activities (12.9%, 11.7-14.1%, 95% CI). Moreover, those who used special equipment for health conditions were also more likely to delay care (23.6%, 20.-27.0%, 95% CI) than

those who did not use special equipment (16.5%, 15.3-17.7%, 95% CI). PopulationHealth Needs Assessment

15 Housing Research shows that residential housing can influence health at several levels: physical condition within the home, conditions in the surrounding neighborhood, and housing affordability.28 Housing is considered affordable when it accounts for less than 30% of the resident’s income. Nearly one-quarter of Oklahomans (24.4%) pay home mortgages that are 30% or more of their incomes. Nearly one in five (18%) of Oklahomans pay 35% or more of their income for housing. Approximately 45% of Oklahomans pay rents at or above 30% of their income and 36.3% pay rents at 35% or more of their income.29 The financial burden of unaffordable housing can prevent Oklahomans from meeting other basic needs like health care or nutritional needs or result in the instability of frequent moves.

Housing insecurity is associated with delay in visiting a doctor, poor or fair health status, limited daily activity, 14 or more days of poor mental health, and smoking.30

Community Concerns To inform the Oklahoma Health Improvement Plan – Healthy Oklahoma 2020, community chats, tribal consultations, and online surveys asked the following essential questions: 1. What is your vision for a healthy community? 2. What are the barriers that prevent us from achieving that vision? 3. How can we address those barriers? The following table summarizes the findings.

COMMUNITY VISION FOR A HEALTHY OKLAHOMA, 2014 General African-American Hispanic Tribal* Access Community focus Family focus Inter-government Healthy foods Safety Health education Collaboration

Physical activity Physical activity outlets Economic development Mind, body, spirit

Health services Economic development Youth key family member Health literacy Health education Education Chronic disease Community Prevention Data

Economic development 7/30/2015 | | Education Transportation Behavioral health *American Indian people residing in the State of Oklahoma are citizens of the state, and as such possess all the rights and privileges afforded by Oklahoma to its citizens. They are also the citizens of tribal nations. Each of the 38 federally recognized Oklahoma tribal nations have inalienable self-governance power over their citizens and territories, and possess unique culture, beliefs, value systems, and history as sovereign nations. Each of the groups listed essential needs for creating a healthy Oklahoma. One theme that emerged from all of the groups was the recognition of the connection between individuals, families, communities, and culture. The Oklahoma Health Improvement Plan acknowledges the dynamic interrelationships between individuals, groups of

individuals, communities, and organizations by framing prevention within a socio-ecological model. PopulationHealth Needs Assessment

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The relationship between poor health outcomes, rates of diseases, and mortality is complex, but reductions in major health challenges such as tobacco use or reduction in health inequity can have rippling effects throughout the population and healthcare system. Just as the America’s Health Rankings® and State Health System Performance Scorecard provided rankings for the State of Oklahoma, the County Health Rankings provided rankings for the counties within Oklahoma. The University of Wisconsin Population Health Institute compiles data from national and state sources into standardized measures that are weighted based upon scientific evidence. Based upon population health, improvement in the measures can help make communities healthier places to live, learn, work, and play. See Appendix – Oklahoma and U.S. Comparison, County Health Rankings for details on the measures and data sources for the state and nation.

31 COUNTY HEALTH RANKINGS ALGORITHM FOR RANKING.

 Health Outcomes represent how healthy a county was compared to other counties in the state.  Length of Life and Quality of Life are the two Health Outcomes measures.  Health Outcomes resulted from the Policies and Programs- influenced Health Factors in communities,  Health Factors represent the influences on the health of the county.  Health Behaviors, Clinical Care, Social& Economic Factors, and

Physical Environment are four Health Factors measures.

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Tobacco use, poor diet, physical inactivity, excessive alcohol consumption, uncontrolled high blood pressure, and hyperlipidemia contribute to most of today’s leading causes of

death.32 PopulationHealth Needs Assessment

17 COUNTY HEALTH OUTCOMES BY QUARTILE RANKING, OKLAHOMA, 2015.33

 Lighter colors indicate better performance in Length of Life and Quality of Life measures.  Table lists the county rankings highlighted in map.

COUNTY HEALTH OUTCOMES RANKING, OKLAHOMA, 2015. County Health County Health County Health County Health Outcome Outcome Outcome Outcome Rank Rank Rank Rank Adair 56 Delaware 31 Lincoln 23 Pittsburg 67 Alfalfa 18 Dewey 34 Logan 7 Pontotoc 47 Atoka 38 Ellis 48 Love 63 Pottawatomie 44 Beaver 14 Garfield 15 Major 52 Pushmataha 72 Beckham 61 Garvin 74 Marshall 25 Roger Mills 10 Blaine 66 Grady 26 Mayes 28 Rogers 8 Bryan 40 Grant 39 McClain 17 Seminole 73 Caddo 46 Greer 27 McCurtain 59 Sequoyah 62 Canadian 6 Harmon 35 McIntosh 70 Stephens 37

Carter 69 Harper 22 Murray 55 Texas 2 7/30/2015 | | Cherokee 45 Haskell 49 Muskogee 71 Tillman 53 Choctaw 68 Hughes 57 Noble 13 Tulsa 20 Cimarron 3 Jackson 42 Nowata 30 Wagoner 16 Cleveland 4 Jefferson 77 Okfuskee 58 Washington 12 Coal 60 Johnston 75 Oklahoma 33 Washita 43 Comanche 24 Kay 32 Okmulgee 65 Woods 9 Cotton 29 Kingfisher 1 Osage 19 Woodward 21 Craig 41 Kiowa 76 Ottawa 54 Creek 51 Latimer 50 Pawnee 64 Custer 11 Le Flore 36 Payne 5

PopulationHealth Needs Assessment

18 19 19

COUNTY HEALTH FACTORS BY QUARTILE RANKING, OKLAHOMA, 2015.

 Lighter colors indicate better performance in Health Behaviors, Clinical Care, Social/Economic Factors, and Physical Environment measures.  Table lists county rankings highlighted in map.

COUNTY HEALTH FACTORS RANKING, OKLAHOMA, 2015. County Health County Health County Health County Health Outcome Outcome Outcome Outcom Rank Rank Rank e Rank

Adair 77 Delaware 56 Lincoln 44 Pittsburg 48 Alfalfa 13 Dewey 14 Logan 18 Pontotoc 34 Atoka 54 Ellis 2 Love 43 Pottawatomie 52 Beaver 21 Garfield 20 Major 8 Pushmataha 76 Beckham 37 Garvin 39 Marshall 46 Roger Mills 23 Blaine 29 Grady 28 Mayes 53 Rogers 12 Bryan 45 Grant 10 McClain 5 Seminole 68 Caddo 66 Greer 41 McCurtain 72 Sequoyah 74 Canadian 4 Harmon 65 McIntosh 67 Stephens 17 Carter 38 Harper 19 Murray 33 Texas 25 Cherokee 55 Haskell 49 Muskogee 64 Tillman 42

7/30/2015 Choctaw 73 Hughes 75 Noble 15 Tulsa 22 | | Cimarron 31 Jackson 30 Nowata 35 Wagoner 16 Cleveland 3 Jefferson 69 Okfuskee 59 Washington 6 Coal 70 Johnston 63 Oklahoma 26 Washita 27 Comanche 47 Kay 32 Okmulgee 58 Woods 1 Cotton 50 Kingfisher 7 Osage 61 Woodward 24 Craig 36 Kiowa 51 Ottawa 60 Creek 57 Latimer 62 Pawnee 40 Custer 11 Le Flore 71 Payne 9

PopulationHealth Needs Assessment

19 B. Clinical Care Common barriers make gaining access to the healthcare system and community care difficult. These barriers can create inequitable health outcomes and often impact certain populations (rural, minority, lower income, etc.) more frequently than others. Two of the major types of barriers to the access to care are financial and structural, which interact with personal/cultural barriers in complicated ways. These barriers to accessing health care may lead to unmet health needs, an inability to receive preventive services, or hospitalizations that could have been prevented.34

Lack of Health Insurance Financial barriers restrict access to care. These barriers may inhibit the ability of an individual or group of individuals to pay for needed medical services. Individuals may delay seeking care because of the costs of services. Uninsured people are less likely to receive medical care.35 The rate of uninsured Oklahoman adults was 17.7% in 2013, 5.0% higher than the national rate.36 Among Oklahomans younger than 65 years of age, 20.6% were uninsured; however, only 10.6% of those less than 19 years were uninsured.37 Additionally, the highest uninsured rate (68.8%) was among Hispanic males aged 18-64 years with incomes less than 138% of the federal poverty level (FPL). Even those with health insurance may experience barriers to accessing health care because they’re underinsured.

PERCENTAGE OF INSURED, UNDERINSURED, AND UNINSURED POPULATION, UNDER-65 POPULATION, OKLAHOMA, 2014.38  Persons at or near poverty levels were 100% 10% more likely to have no insurance or be 90% 16% 1% 30% 31% underinsured. 80% 6%  For this study, underinsured meant 70% persons were insured but spent a high 60% 19% 30% share of annual income on medical care. 50% 89%  In total, there were 1,014,452 40% 78% 30% Oklahomans who were uninsured or 50% 20% 40% underinsured. 10%  The federal poverty level takes into 0% account family size. For example, 100% <100% 100-199% 200-399% 400%+ FPL was $11,670 for a single individual, $15,730 for 2 persons, and $23,850 for Insured Underinsured Uninsured a family of four in 2014.39

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Health Provider Access Structural barriers impede access to care. These barriers relate to the number, type, concentration, location, or organizational structure of healthcare providers. People who do not have a personal physician or healthcare provider most often rely upon local emergency rooms (ER) and hospital outpatient clinics for their care. Preventive care and continuity of care for a medical problem most often is missing from care delivered in the ER or outpatient setting. Only 75% of adults in Oklahoma (35th in the nation) reported that they have a usual source of care.40

The number of primary care physicians in Oklahoma has continued to increase from 78.5 per 100,000 population in 2005 to 84.8 per 100,000 in 2014; however Oklahoma still ranks 48th in the nation.41 Massachusetts, the highest ranking state for primary care physicians, has 200.8 per 100,000 population. 42 In rural areas of Oklahoma, 40% of

the population was reported to be served by 28% of the 3,660 primary care physicians in Oklahoma.43 Another study PopulationHealth Needs Assessment

20 21 21

found that while the entire physician workforce The Health Resources and Services Administration (HRSA) is aging, primary care physicians in rural designates medically underserved areas and health professional Oklahoma were older compared to their urban shortage areas, a prerequisite for participation in programs like counterparts.44 the National Health Service Corps Scholarship and loan repayment programs, Medicare rural provider enhanced The availability of primary care providers is payments, and eligibility for federal assistance of federally important to improve the health of the qualified health center, and rural health clinics. population. Areas where healthcare is hard to find are known as Health Professional Shortage Areas (HPSAs). Shortages of health professionals include the disciplines of primary care (general or family practice, general internal medicine, pediatrics, or obstetrics and gynecology), dental, and mental health (psychiatrists, clinical psychologists, clinical social workers, psychiatric nurse specialists, and marriage and family therapists). HPSAs designations may be based upon geographic, demographic, or facility locations; moreover, members of federally recognized American Indian tribes are automatically designated as population group level HPSAs. Designations are made every 3-4 years. Additionally, Indian Health Service has established 12 federally-owned health services facilities in Oklahoma to serve eligible members of federally recognized American Indian and Alaskan Native tribes. Also, Tribal Nations within Oklahoma established 38 tribally-owned health service facilities.

PRIMARY CARE HEALTH PROFESSIONAL SHORTAGE AREAS BY TYPE, OKLAHOMA, 2015.45  Oklahoma has 162 total HPSAs: Geographic area=7, Population group=56, and Facility=99.  Population within the HPSAs is 2,271,706 (58.6% of Oklahomans).46

 68.5% of need is being met.  102 additional

practitioners are

7/30/2015 | | required to meet the full need. 47

Dental Health Professional Shortage Areas (HPSAs) signify a shortage of oral health professionals. This ratio is usually 5,000 residents to 1 non-federal dentist. As of 2015, 98 Dental Care HPSAs have been designated in Oklahoma: geographic=3, Population=6, and Facility =89. Only 235,387 individuals reside in the designated nine county areas with 81% of need met. An addition al 17 practitioners are needed to achieve a population-to-provider ratio above a shortage level.

Mental Health Professional Shortage Areas are another form of HPSA that have a shortage of mental health professionals. Mental Health HPSAs are designated using several criteria, including population-to clinician ratios. This ratio is usually 30,000 residents to 1 mental health professional. Oklahoma has 107 designated mental health HPSAs: PopulationHealth Needs Assessment Geographic=4, Population=6, and Facility=97. With Mental Health HPSAs in 69 counties only 36.7% of the need

21 was met. Sixty additional mental health providers are needed to achieve a population-to-provider ratio below the thresholds necessary for designation.

Medically Underserved Areas and Populations (MUA/MUP) have too few primary care providers, high infant mortality rates, a high population percentage of poverty, and/or a high percentage of elderly in the population. MUA/MUP designations do not expire. This designation is a way to identify extreme local issues that may not show up at a larger, county-level scale. MEDICALLY UNDERSERVED AREAS AND POPULATIONS, OKLAHOMA, 2015.48  Residents experienced a shortage of personal health services in parts of 66 counties.49  The Governor designated a MUA because of health literacy and poverty barriers to personal health services.

Health System Performance The Agency for Healthcare Research and Quality’s Prevention Quality Indicators (PQI measures) were developed to monitor health system performance over time, across regions, and among populations using patient data found in a typical hospital discharge abstract. The Prevention Quality Indicators (PQIs) are a set of measures that can be used to identify quality of care for "ambulatory care sensitive conditions," conditions for which good outpatient care can potentially prevent the need for hospitalization or for which early intervention can prevent complications or more

severe disease. Preventable hospitalizations data based on adjusted-PQI and state hospital discharge data were aggregated by disease/condition and geographic area for use in quality improvement.

In Oklahoma, an estimated 45,355 hospital stays could have been prevented in 2013. The preventable hospitalization

conditions were: diabetes (short-term complications, long-term complications, uncontrolled, and lower-extremity 7/30/2015 | | amputation), chronic obstructive pulmonary disease, adult asthma, hypertension, heart failure, angina without a cardiac procedure, dehydration, bacterial pneumonia, or urinary tract infection.

Pneumonia can be a complication especially among infants or persons with other chronic conditions, such as diabetes, heart disease, chronic obstructive

pulmonary disease (COPD), cancer, or asthma.50 PopulationHealth Needs Assessment

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The PQI overall composite was set as a single composite to capture the concept of preventable or avoidable hospitalizations for acute and chronic diseases and conditions. As expressed non-academically, the measure answers “how well providers prevent patients from having serious health problems.”51 The PQI overall composite is presented as a rate per 100,000 population aged 18 years and older and county of residence.

PREVENTION QUALITY INDICATOR OVERALL COMPOSITE RATE PER 100,000 POPULATION, OKLAHOMA, 2013.  The median rate was 1,635.5 in Mayes County.  Sequoyah County had the lowest rate at 599.7, followed by Cleveland (644.2), Coal (690.1), Washington (889.6), Beaver (870.0), Cherokee (890.8), and Grady (980.8) counties.  Kiowa County had the highest rate at 4,376.5, followed by Choctaw (3,538.8), Harmon (3,491.7), Pushmataha (2,880.7), and Marshall (2,620.1) counties.

The PQI chronic composite helps answer “How often patients are admitted to a hospital because a long-lasting (or chronic) condition is causing health problems.” The PQI chronic composite is a risk adjusted rate per 100,000 population aged 18 years and older by county of residence. The chronic composite includes admissions for one of the following conditions: diabetes (short-term complications, long-term complications, uncontrolled, and lower- extremity amputation), chronic obstructive pulmonary disease, adult asthma, hypertension, heart failure, or angina

without a cardiac procedure.

PREVENTION QUALITY INDICATOR CHRONIC COMPOSITE RATE PER 100,000 POPULATION, OKLAHOMA, 2013.  The median rate was 866.2 per 100,000

7/30/2015 population in Ellis | | County.  Sequoyah County had the lowest rate of 301.0, followed Beaver (336.9), Cleveland (404.9), Texas (473.6), and Coal (507.0) counties.  Kiowa County had the highest rate at 2,860.1 per 100,000 population,

PopulationHealth Needs Assessment followed by Harmon (2,513.2), Choctaw (2,331.3), Adair (1,764.7), and Johnston (1,450.9) counties.

23 C. Health Costs Prevention Quality Indicators and Composites were used by the MONAHRQ® web query system to provide estimated cost savings through the reduction of a percentage of preventable hospitalizations. While not all avoidable, an estimated 45,355 stays for acute and chronic diseases and conditions were identified as potentially preventable through outpatient services in Oklahoma during 2013. The cost savings was also calculated.

ESTIMATED COST SAVINGS BY % REDUCTION IN PREVENTABLE HOSPITALIZATIONS, OKLAHOMA, 2013.  Nearly $43 million in $200,000,000 hospitalization costs could be saved $150,000,000 with only a 10% decrease in both $100,000,000 acute and chronic-related preventable hospitalizations. $50,000,000  A 50% reduction equated to $0 more than a $213 million dollar 10% 20% 30% 40% 50% savings.

Diabetes, hypertension, and chronic obstructive pulmonary disease (primarily caused by smoking) were among the preventable hospitalization diseases and conditions. More than half of the cost savings from the overall composite come from chronic diseases and conditions. In 2013, nearly 27,000 hospitalizations for chronic disease and conditions could have been avoided. A 10% reduction in that number would save an estimated $26 million dollars.

PREVENTION QUALITY INDICATOR, CHRONIC DISEASE/CONDITION COMPOSITE RATE PER 100,000 POPULATION AGED 18 YEARS AND OLDER BY COUNTY OF RESIDENCE AND ESTIMATED COST SAVINGS, OKLAHOMA, 2013.

County # of Risk- Annual Savings per Percentage Reduction in Number Stays Adjusted 10% 20% 30% 40% 50% Rate Adair 300 1,764.7 $117,077 $234,154 $351,232 $468,309 $585,387 Alfalfa 32 577.7 $37,578 $75,157 $112,736 $150,315 $187,894 Atoka 170 1,402.8 $144,174 $288,348 $432,522 $576,696 $720,870 Beaver 16 336.9 $22,096 $44,193 $66,289 $88,386 $110,483

Beckham 148 899.3 $199,605 $399,211 $598,817 $798,422 $998,028 Blaine 79 861.4 $96,382 $192,765 $289,148 $385,531 $481,913 Bryan 512 1,420.4 $462,111 $924,223 $1,386,335 $1,848,446 $2,310,558

Caddo 178 772.5 $142,639 $285,278 $427,917 $570,556 $713,195

7/30/2015 | | Canadian 491 577.4 $503,020 $1,006,040 $1,509,060 $2,012,080 $2,515,100 Carter 515 1,336.1 $560,362 $1,120,725 $1,681,088 $2,241,450 $2,801,813 Cherokee 205 557.6 $204,960 $409,921 $614,881 $819,842 $1,024,803 Choctaw 320 2,331.3 $255,837 $511,674 $767,512 $1,023,349 $1,279,187 Cimarron c c c c c c c Cleveland 721 404.9 $813,925 $1,627,850 $2,441,776 $3,255,701 $4,069,626 Coal 27 507.0 $33,217 $66,434 $99,651 $132,868 $166,086 Comanche 643 794.9 $609,666 $1,219,333 $1,829,000 $2,438,667 $3,048,334 Cotton 41 786.4 $30,534 $61,068 $91,603 $122,137 $152,672 Craig 172 1,266.4 $131,079 $262,158 $393,237 $524,317 $655,396 Creek 644 1,080.4 $623,870 $1,247,740 $1,871,611 $2,495,481 $3,119,352

Custer 169 808.6 $172,750 $345,500 $518,251 $691,001 $863,752 PopulationHealth Needs Assessment

24 25 25

County # of Risk- Annual Savings per Percentage Reduction in Number Stays Adjusted 10% 20% 30% 40% 50% Rate Delaware 234 549.9 $214,117 $428,235 $642,353 $856,471 $1,070,589 Dewey 31 672.2 $36,751 $73,502 $110,253 $147,004 $183,755 Ellis 34 875.8 $23,797 $47,594 $71,391 $95,188 $118,985 Garfield 550 1,079.6 $649,431 $1,298,862 $1,948,293 $2,597,724 $3,247,156 Garvin 180 746.9 $134,647 $269,295 $403,943 $538,591 $673,239 Grady 243 578.2 $235,580 $471,159 $706,739 $942,319 $1,177,899 Grant 66 1,415.7 $72,640 $145,281 $217,921 $290,562 $363,202 Greer 78 1,374.8 $66,722 $133,445 $200,168 $266,891 $333,614 Harmon 60 2,513.2 $63,111 $126,223 $189,334 $252,446 $315,558 Harper 37 1,077.5 $24,517 $49,034 $73,551 $98,068 $122,585 Haskell 64 549.1 $54,420 $108,841 $163,262 $217,683 $272,104 Hughes 73 604.3 $69,621 $139,242 $208,864 $278,485 $348,106 Jackson 265 1,378.1 $251,758 $503,517 $755,276 $1,007,034 $1,258,793 Jefferson 73 1,230.6 $77,788 $155,577 $233,365 $311,154 $388,942 Johnston 137 1,450.9 $117,951 $235,902 $353,853 $471,804 $589,755 Kay 497 1,207.6 $415,270 $830,540 $1,245,811 $1,661,081 $2,076,351 Kingfisher 93 728.8 $148,754 $297,508 $446,262 $595,016 $743,771 Kiowa 232 2,860.1 $153,044 $306,089 $459,133 $612,178 $765,223 Latimer 98 957.2 $80,257 $160,515 $240,773 $321,030 $401,288 Le Flore 279 662.0 $162,173 $324,346 $486,519 $648,692 $810,866 Lincoln 225 780.8 $234,702 $469,405 $704,108 $938,811 $1,173,514 Logan 191 591.2 $202,998 $405,996 $608,994 $811,992 $1,014,990 Love 58 694.5 $57,776 $115,552 $173,328 $231,104 $288,880 Major 38 519.1 $44,617 $89,235 $133,853 $178,471 $223,089 Marshall 193 1,223.6 $148,483 $296,967 $445,451 $593,935 $742,418 Mayes 355 1,009.5 $455,298 $910,597 $1,365,896 $1,821,195 $2,276,494

McClain 172 625.3 $131,765 $263,530 $395,296 $527,061 $658,827 McCurtain 233 861.8 $180,891 $361,782 $542,673 $723,564 $904,455 McIntosh 191 883.6 $187,767 $375,535 $563,303 $751,071 $938,839

Murray 106 866.1 $110,703 $221,406 $332,109 $442,812 $553,516

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Muskogee 687 1,199.4 $787,020 $1,574,040 $2,361,060 $3,148,081 $3,935,101 Noble 84 809.1 $98,084 $196,169 $294,254 $392,338 $490,423 Nowata 55 556.6 $69,177 $138,355 $207,533 $276,711 $345,888 Okfuskee 103 975.6 $121,432 $242,864 $364,297 $485,729 $607,161 Oklahoma 5,297 994.6 $5,056,118 $10,112,237 $15,168,356 $20,224,475 $25,280,594 Okmulgee 421 1,245.3 $400,589 $801,179 $1,201,768 $1,602,358 $2,002,947 Osage 351 852.1 $362,935 $725,870 $1,088,805 $1,451,740 $1,814,675 Ottawa 253 904.7 $204,199 $408,398 $612,598 $816,797 $1,020,996 Pawnee 134 915.7 $134,423 $268,846 $403,269 $537,692 $672,115 Payne 413 829.3 $434,004 $868,009 $1,302,014 $1,736,019 $2,170,023 Pittsburg 423 1,002.2 $337,254 $674,508 $1,011,762 $1,349,016 $1,686,270

Pontotoc 196 644.5 $220,438 $440,876 $661,314 $881,752 $1,102,190 PopulationHealth Needs Assessment

25 County # of Risk- Annual Savings per Percentage Reduction in Number Stays Adjusted 10% 20% 30% 40% 50% Rate Pottawatomie 703 1,273.4 $635,578 $1,271,157 $1,906,736 $2,542,315 $3,177,893 Pushmataha 158 1,373.1 $100,897 $201,794 $302,691 $403,588 $504,486 Roger Mills 28 855.9 $36,808 $73,616 $110,424 $147,233 $184,041 Rogers 498 701.2 $520,038 $1,040,077 $1,560,116 $2,080,155 $2,600,194 Seminole 232 1,086.0 $233,880 $467,760 $701,640 $935,520 $1,169,400 Sequoyah 105 301.0 $85,268 $170,536 $255,804 $341,072 $426,340 Stephens 422 1,029.7 $357,909 $715,819 $1,073,728 $1,431,638 $1,789,547 Texas 62 473.6 $78,792 $157,584 $236,376 $315,168 $393,960 Tillman 84 1,274.9 $63,459 $126,919 $190,379 $253,839 $317,299 Tulsa 4,692 1,039.5 $4,740,970 $9,481,940 $14,222,911 $18,963,881 $23,704,852 Wagoner 515 907.6 $471,271 $942,542 $1,413,814 $1,885,085 $2,356,356 Washington 278 566.3 $328,769 $657,538 $986,308 $1,315,077 $1,643,847 Washita 106 1,032.8 $103,487 $206,974 $310,461 $413,948 $517,435 Woods 41 556.4 $52,582 $105,164 $157,746 $210,329 $262,911 Woodward 165 1,035.0 $174,456 $348,913 $523,370 $697,827 $872,284 *Cost savings are based on charges that have been adjusted to costs, using hospital-specific cost-to-charge ratios. Values based on 10 or fewer discharges are designated with a "c" to protect confidentiality of patients.

Identification of High User Groups Limited data was available that illustrated the identification of a group of high volume health care users. The purpose was to investigate care coordination as a way to help high volume users better manage their health. The report, Feasibility of Care Coordination for Persons Dually Eligible for Medicaid and Medicare dated December 201352, examined dual-eligibles, people who are covered by both Medicare and Medicaid at the same time. Most primary and acute care service costs were covered by Medicare while long-term care and community-based services were covered by Medicaid funds. Most, but not all, dual-eligibles are low income individuals aged 65 years or older.

According to the report, more than 109,000 Oklahomans were dual-eligible. Dual eligibles accounted for 14% of the Medicaid enrollment population, but accounted for a disproportional 32% of Medicaid expenditures. However, dual eligibles were further defined into four populations based on need:

 Frail elders and persons with physical disabilities in long-term care accounted for 26% of the dual population 7/30/2015 | | but 79% of expenditures.  Persons with intellectual or development disabilities in long-term care accounted for 1% dual population, but 10% of expenditures.  Adults with chronic physical and/or mental health needs in the community accounted for 48% of the dual population and 20% of expenditures.  Healthy seniors residing in the community accounted for 25% of the dual population and 1% of expenditures.

The report concluded with the exploration of coordinated care to rebalance services in a manner that improves member quality of life and health outcomes and yields significant savings, particularly with respect to long-term care

recipients. PopulationHealth Needs Assessment

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4. Tobacco Use Smoking has long been associated with the development of cancer. Moreover, smoking has been found to increase the risk of developing diabetes and/or hypertension.53 Tobacco contributed to an estimated 80-90% of lung cancer deaths and 90% of deaths from chronic lower respiratory disease. Health care costs associated with smoking were approximately $1.62 billion dollars with $264 million covered by the Medicaid program54. Oklahoma has consistently had one of the highest rates of adult and youth smoking in the nation. Additionally, 21.2% of male high school students used smokeless or spit tobacco in 2013 compared to 14.7% of U.S. males students. The Tobacco Free Kids Campaign estimates that each year 4,000 youths in Oklahoma become daily smokers.

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Tobacco Use Adult Smoking 23.7% 18.8% 18.0% Prevalence (2013) (2013) 15.1% High School 12.7% High School 10.0% Youth Smoking (2013, unweighted) (2013) 2.0% Prevalence 4.8% Middle School 2.9% Middle School (2013) (2013)

According to American’s Health Rankings® 2014, Oklahoma was among the five worst states for smoking prevalence, ranking 45th and with a rate 26% higher than the national average. However, over the decades, the rate of smoking in Oklahoma has declined with the trend slowing in the most recent years.

TRENDS IN HEALTH METRICS.  The Behavioral Risk Factor 30.0% 26.1% 23.3% 23.7% Surveillance System results on 25.0% OK2SHARE calculated the 20.0% 15.1% confidence intervals for the 15.0% responses. 10.0% 4.8%  The 2011 rate of 26.1% (24.7- 5.0% 27.5%, 95% CI) was not 0.0%

7/30/2015 statistically different than the Adult Current Smoker High School Smoker Middle School Smoker | | 2013 rate of 23.7% (22.4-25.0%, 2011 2012 2013 95% CI).

The youth prevalence of current smokers was determined by the Youth Tobacco Survey, administered to public school students in both middle school and high school. The Youth Risk Behavior Survey, conducted in conjunction with the Centers for Disease Control and Prevention, has been conducted as a weighted survey to obtain representative data for states and the nation. Since the unweighted data may vary from the weighted data, it should be noted that the high school data is unweighted and the middle school data is weighted.

PopulationHealth Needs Assessment

27 A. Population Health The burden of tobacco use varied across Oklahoma. While limited information was publicly available on youth tobacco use in Oklahoma, more was available on adult smokers. The 2013 rate of current smokers in the northwestern Oklahoma of 19.3% (16.2-22.5%, 95% CI) was lower than the state rate of 23.7% (22.4-25.0%, 95% CI). The rates in the other regions of the state were not statistically different than the state rate: Tulsa area (20.4%), Central (21.2%), Southeast (26.7%), Southwest (28.9%), or Northeast (26.3%). The rate of current smokers ranged from a low of 15.6% in Noble County to a rate 2.7 times higher (43.0%) in Greer County.

PERCENTAGE OF SMOKERS BY COUNTY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.  Lowest rates of adult smokers were in Noble (15.6%), Payne (16.5%), Cleveland (17.6%), Rogers (17.7%), and Kingfisher (17.8%) counties.  Counties with the highest rates were: Atoka (35.0%), Creek (35.5%), Adair (36%), Jefferson (37%), Alfalfa (37.8%), and Greer (43.0%). The rates of current smokers were not statistically different between males (25.8%, 23.8-27.8%, 95% CI) and females (21.7%, 20.1%-23.4%). The rates of current smoker varied also by race, educational status, age, and income.

RATES OF CURRENT SMOKERS BY CHARACTERISTICS, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.

 The rates of current smokers were Overall 23.7% markedly lower among $50k+ 13.6% $35k-49999 22.1% those with higher $25k-34999 26.6% $15k-24999 7/30/2015

income, 65 years and 30.7% | older, college <$15000 41.4% 65 + yrs graduates, and other 9.9% 35-64 yrs 26.3% race. 18-34 yrs 28.3%  As such, some rates College Grad. 8.7% were markedly higher, Some College 23.3% H.S. 26.8% especially among Less than H.S. 38.8% American Indians and Other 14.4% those with lower Multicultural 33.4% socio-economic status. Hispanic 18.8% Amer.Indian 37.7% Black 25.9%

White 22.3% PopulationHealth Needs Assessment

28 29 29

B. Clinical Care Ever since the U.S. Surgeon General’s first landmark report on smoking in 1964, the serious effects that smoking has on health continues to grow. Data from the 2013 Oklahoma Behavioral Risk Factor Surveillance Survey showed that even Oklahomans with health conditions caused or exacerbated by smoking struggle with addiction to tobacco.

21.2% of adults who were told by a doctor that they had diabetes were current smokers. 24.9% of adults who were told by a doctor that they had pre-diabetes were current smokers. 23.4% of adults who reported having high blood pressure were current smokers. 20.9% of adults who were talking medicine for high blood pressure were current smokers. 21.9% of adults who were cancer survivors were current smokers. 32.8% of adults who were told they had a heart attack were current smokers. 23.5% of adults who had an angina diagnosis were current smokers. 31.0% of adults who were told by a doctor that they had a stroke were current smokers. 29.4% of adults with asthma were current smokers. 47.3% of adults with chronic obstructive pulmonary disease, emphysema, or chronic bronchitis were current smokers.

Smoking causes an increased risk of death from lung cancer, chronic obstructive pulmonary disease, ischemic health disease, stroke, and all causes combined.55

Population-Based Cessation Service

The 2013 Behavioral Risk Factor Surveillance System found that 52.2% of current smokers in the U.S. quit smoking for at least one day during the past 12 months. As with other states, Oklahoma has provided a tobacco helpline for

more than a decade to assist with quit attempts.

In Fiscal Year (FY) 14, the Oklahoma Tobacco Research Center at the University of Oklahoma Health Sciences

Center evaluated the utilization, satisfaction levels, and effectiveness of the Oklahoma Tobacco Helpline. During the 7/30/2015

| | year, there were 24,982 total callers of which almost 24,000 tobacco users received services. There was a reported

29% decline in services provided from FY 2013. Conversely, fax referrals from health professionals to the Helpline increased 38% from FY 2013. Nearly 4,000 fax referrals were received by the Helpline.

Most (93%) of the tobacco users who enrolled in the multiple-call program received nicotine replacement therapy from the Helpline and nearly half (49%) of those who enrolled in the single-call program also received nicotine replacement therapy from the Helpline. In FY 2014, tobacco users from all 77 counties contacted the Helpline. Contact at seven months post intervention found:

 A single Helpline call plus two weeks of nicotine replacement therapy (available to tobacco users with health insurance) achieved 30-day quit rates of 21.4% (95% CI: 14.3-28.6%) and  Of those who received the multiple-call intervention, 36.6% (95% CI: 31.9-41.3%) reported a 30-day

PopulationHealth Needs Assessment abstinence.

29 C. Health Costs Oklahomans spend approximately $1.62 billion per year on smoking-related health costs.56 The assessment focused on preventable hospitalizations for chronic obstructive pulmonary disease and angina.

Tobacco use is the primary cause of chronic obstructive pulmonary disease in the U.S. secondhand smoke was associated with a 10-43% increase in the risk of chronic obstructive pulmonary disease, according to the American Lung Association. Moreover, nearly all of the deaths from chronic lower respiratory disease were from chronic obstructive pulmonary disease.57

Smoking, socioeconomic status, disease severity, and age are associated with higher hospital admissions. With access to high quality community and clinical care, hospitalizations from chronic obstructive pulmonary disease often are preventable. Geographic areas with high rates of hospital admissions also tend to have high rates for other diseases that require ongoing self-management and medical care.58

Poorer health literacy is associated with greater COPD severity and higher likelihood of hospitalizations.59

In Oklahoma, an estimated 10,187 hospitalization from chronic obstructive pulmonary disease could have been prevented in 2013, according to the MONAHRQ® web-based query. If 10% of the hospitalizations were prevented through outpatient care and community services, an estimated $9,079,282 in cost savings could have been achieved.

COST SAVINGS BY PERCENT REDUCTION IN THE NUMBER OF CHRONIC PULMONARY DISEASE HOSPITALIZATIONS, OKLAHOMA, 2013. 10% 20 30% 40% 50% $9,079,282.29 $18,158,564.54 $27,237,846.88 $36,317,129.12 $45,396,411.41

Preventable hospitalizations from chronic obstructive pulmonary disease were generally higher in the southern

portion of the state. While 71 counties experienced ten or more hospitalizations, risk-adjusted rates were highest in ten counties. Similarly, the ten counties with the largest number of hospitalizations stood apart from the others.

TOP TEN COUNTIES BY RATE AND BY NUMBER OF PREVENTABLE HOSPITALIZATIONS, OKLAHOMA, 2013.

County Risk-Adjusted County Number of 7/30/2015

Rate/100,000 Stays |

Harmon 1,965.9 Oklahoma 1,811 Kiowa 1,759.9 Tulsa 1,557 Choctaw 1,527.8 Muskogee 285 Adair 1, 473.1 Creek 278 Grant 1,194.4 Cleveland 266 Bryan 1,107.3 Bryan 250 Atoka 1,103.4 Pottawatomie 245 Pushmataha 1,025.2 Carter 241 Carter 1,001.3 Garfield 232 Marshall 994.2 Kay 217 PopulationHealth Needs Assessment 30 31 31

Smoking has been associated with more hospitalizations among those with angina. Angina is chest pain that occurs when a narrowing or blockage of a coronary artery prevents sufficient oxygen-rich blood from reaching the heart muscle. Both stable and unstable angina are symptoms of potential coronary artery disease. Effective management of coronary artery disease reduces the occurrence of major cardiac events such as heart attacks, and may reduce hospital admission rates for angina. Individual risk factors associated with angina and higher hospital admissions included: smoking, high cholesterol, high blood pressure, diabetes, elderly age (over 70), and male gender. Population risk factors associated with angina and higher hospital admissions included:

 Access to care Areas with high rates of angina admissions also tend to have high rates of admissions for other disease that require ongoing self-management and medical care. Combining emergency room and inpatient chart data may give a more accurate picture of the issues in the community.

 Lower socio-economic areas Studies have found that low-income ZIP code areas have nearly 2-3 times the rate of angina hospitalizations than high-income ZIP code areas.60

Preventable hospitalizations for angina without procedure were limited to 11 of Oklahoma’s 77 counties. The highest risk-adjusted rate of preventable hospitalization occurred in Latimer County, while the highest number of preventable hospitalizations for angina without procedure occurred in Oklahoma County.

PREVENTABLE HOSPITALIZATIONS AMONG THOSE WITH TEN OR MORE ADMISSIONS FOR ANGINA WITHOUT PROCEDURE, OKLAHOMA, 2013. Cost Saving by Percent Reduction in # of Stays Risk- # of County Adjusted 10% 20% 30% 40% 50% Stays Rate Bryan 12 33.91 $5,531.23 $11,062.46 $16,593.70 $22,124.93 $27,656.16 Carter 15 38.79 $9,252.76 $18,505.53 $27,758.30 $37,011.06 $46,263.82 Latimer 25 253.77 $17,662.55 $35,325.10 $52,987.65 $70,650.20 $88,312.75

Le Flore 14 33.05 $6,393.03 $12,786.06 $19,179.09 $25,572.12 $31,965.15

Muskogee 15 26.27 $12,010.50 $24,021.00 $36,031.50 $48,042.00 $60,052.50 Oklahoma 71 13.38 $58,917.72 $117,835.43 $176,753.15 $235,670.87 $294,588.58 Ottawa 12 44.38 $6,808.70 $13,617.41 $20,426.11 $27,234.82 $34,043.52

7/30/2015 Payne 21 44.43 $17,581.49 $35,162.99 $52,744.48 $70,325.98 $87,907.47 | | Pittsburg 12 28.96 $6,443.76 $12,887.52 $19,331.28 $25,775.04 $32,218.80 Pottawatomie 11 19.9 $7,468.68 $14,937.36 $22,406.04 $29,874.72 $37,343.40 Tulsa 46 10.18 $35,140.00 $70,280.00 $105,419.99 $140,559.99 $175,699.99 Total 254 n.a. $183,210.42 $366,420.86 $549,631.29 $732,841.73 $916,052.14

The cost savings from reducing the number of preventable hospital stays ranged from $183,210 for a 10% reduction

to more than $900,000 for a 50% reduction. PopulationHealth Needs Assessment

31 5. Obesity With 32.5% of its residents obese, Oklahoma was the 6th most obese state in the nation in 2013 according to the Behavioral Risk Factor Surveillance System. Among Oklahoma’s youth, 11.8% were obese according to the Youth Risk Behavior Survey in 2013. One-third of adult Oklahomans did not participate in leisure time physical activity in 2013.

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Obesity 32.5% 29.4% 29.5% Adult Obesity (2013) (2013) 11.8% High School 13.7% High School 10.6% Youth Obesity (2013) (2013) Adult No Leisure Time 33.0% 26.3% 20.8% Activity (2013) (2013) Adult Fruit 49.6% at least 1/day 60.8% at least 1/day 50.0% at least 1/day Consumption (2013) (2013) Adult Vegetable 1.5/day 1.6/day 2.1 per day Consumption (2013) (2013) Food Desert/Food 21.1% of population 12.3% of population Under development Availability (2013) (2013)

Over time, the rate of adult obesity has climbed; however, the 95% confidence intervals of the annual data overlap, meaning that there were no statistical differences between the 2011, 2012, and 2013 percentages of obesity:  2011: 31.1% (29.7-32.5%, 95% CI);  2012: 32.2% (30.8-33.6%, 95% CI); and  2013: 33.5% (31.2-33.9%, 95% CI).

There was a statistical difference between the youth obesity rates in 2011 (16.7%, 13.9-19.9%, 95% CI) and 2013

(11.8%, 10.0-14.0%, 95% CI). The 2011 and 2013 percentages of adults with no leisure time physical activity were not statistically different. Similarly, the fruit and vegetable consumption among adults remained steady from 2011 to 2013.

TRENDS IN HEALTH METRICS.

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60.0% 50.2% 50.4% 50.0% 40.0% 32.2%32.5% 33.0% 31.1% 31.2%28.3% 30.0% 26.8% 25.3% 16.7% 20.0% 11.8% 10.0% 0.0% Adult Obesity Youth Obesity Adult No Leisure Adult at least 1 Adult at least 1 Time Activity fruit/day vegetable/day

2011 2012 2013 PopulationHealth Needs Assessment

32 33 33

A. Population Health The rate of obesity in Oklahoma has increased from 1 in 7 adults in 1995 to 1 in 3 adults in 2013. Kingfisher had the lowest rate (24.6%) and Johnston had the highest rate (44.8%) of adult obesity.

PERCENTAGE OF OBESE ADULTS BY COUNTY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.  Twenty-two counties had an adult obesity rate lower than the state rate (31.1%).  Eighteen counties had similar rates as the state rate (range 31.1- 33.9%).  Thirty-seven counties had adult obesity rates higher than the state rate (range 34.0-44.8%). Males (32.9%) and females (32.2%) had similar rates of obesity. Those with incomes of $75,000 or greater had lower obesity rates (31.3%, 28.5-34.3% 95% CI) than those with incomes less than $15,000 (36.4%, 32.4-40.6%, 95% CI); however the rates were not statistically different. The rates of adult obesity varied by educational status. Those with less than a high school education (35.6%, 31.3-40.2, 95% CI) and those who were high school graduates (33.9%, 31.4-36.4%, 95% CI) had higher rates of obesity compared to those who were college graduates (28.3%, 26.1-30.6%, 95% CI). Adult obesity rates also varied by race and ethnicity. The 2013 obesity rates among adults by race/ethnicity were:  31.6% (30.1-33.2, 95% CI) among Whites;  35.8% (30.6-41.4, 95% CI) among Blacks;  34.4% (28.8-40.5, 95% CI) among Hispanics;

 18.3% (10.0-31.0%, 95% CI) among Asians;  41.3% (35.1-47.7%, 95% CI) among American Indians; and  29.9% (24.7-35.7%, 95% CI) among those who reported two or more races.

7/30/2015 The adult obesity rates among Blacks and American Indians were statistically different than the rate among Whites.

| |

In 2013, 67.9% of adults in Oklahoma were either obese or overweight. Among Oklahoma’s youth, 27.1% were either obese or overweight. In addition to weight status, physical activity and nutrition play a role in state health.

INDICATORS OF WEIGHT STATUS, OKLAHOMA, 2013.61 Indicator Data 95% CI Year Data Source

Adults who are obese 32.5% (31.2-33.9) 2013 1

Adults who are overweight 35.4% (34.0-36.8) 2013 1 High School-aged youth who are obese 11.8% (10.0-14.0) 2013 2 High School-aged youth who are overweight 15.3% (13.1-17.8) 2013 2

Date sources: 1=Behavioral Risk Factor Surveillance System, 2=Youth Risk Behavior Survey PopulationHealth Needs Assessment

33 Physical Activity Physical activity can play a role in reversing or preventing health problems and help reduce the risk of premature death. Ranked 47th in the nation in 2013, 33% of Oklahomans reported no leisure time physical activity compared to a national average of 26.3%.62 According to the Oklahoma Health Improvement Plan, Healthy Oklahoma 2020, adults who engage each week in 150 minutes of moderate to vigorous intensity aerobic activity in bouts of at least 10 minutes experience improved health and fitness and reduced risk of several chronic diseases; yet, close to 23% of U.S. adults do not engage in any type of physical activity.

PERCENT OF NO LEISURE TIME PHYSICAL ACTIVITY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.  The 2013 Behavioral Risk Factor Surveillance System found that Dewey County had the highest rate of no leisure time physical activity (49.7%), while Murray County had the highest rate (22.4%).

Several indicators of physical activity were found in addition to the percentage of no leisure time activity.

INDICATORS OF PHYSICAL ACTIVITY, OKLAHOMA, 2013.63 Indicator Data 95% CI Year Data Source

Adults who engage in no leisure time physical activity 33.0% (31.6-34.3) 2013 1

Adults aerobically active 150 minutes 43.9% (42.5-45.4) 2013 1

Adults aerobically active 300 minutes 27.4% (26.1-28.7) 2013 1

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Adults meeting muscle strengthening guidelines 24.3% (23.0-25.6) 2013 1

Adults meeting aerobic and muscle strengthening 15.8% (14.7-17.0) 2013 1 guidelines

Adolescents who are physically active daily 38.5% (35.2-41.9) 2013 2

Adolescents who participate in daily physical 32.2% (27.6-37.1) 2013 2 education

2009- Adults who usually walk or bike to work 2.1% (1.9-2.2) 3

2011 PopulationHealth Needs Assessment

34 35 35

Indicator Data 95% CI Year Data Source

State guidance on policy for joint-use of school No N/A 2012 4 facilities

Youth with parks/rec centers/sidewalks in 2011- 40.6% N/A 5 neighborhoods 2012

Adults with at least one park within 1/2 mile 33.4% N/A 2010 6

State guidance on policies for physical activity in PE Yes N/A 2012 4 class

State guidance on policies for school recess Yes N/A 2012 4

State guidance on policies for walking/biking to/from Yes N/A 2012 4 school

State has adopted some form of a Complete Streets policy No N/A 2012 7

State requires physical activity for child care (preschool) No N/A 2012 8

Date sources: 1=Behavioral Risk Factor Surveillance System, 2=Youth Risk Behavior Survey, 3= American Community Survey, 4= SHPPS, 5= National Survey of Children's Health, 6= National Survey of Children's Health, 7= National Complete Street Coalition, and 8=State Indicator Report on Physical Activity Nutrition Eating more fruits and vegetables can lower the risk of some chronic diseases including cancers, diabetes, heart disease, and obesity.64 Nationally, the rate of adults who consumed fruit less than one time daily was 38.5% in 2013. Oklahoma’s rate of 50.4% was next to last among the states.65 Meaning, half of adults in Oklahoma did not eat even one piece of fruit each day in 2013.

PERCENT OF THOSE WHO CONSUMED FRUIT LESS THAN ONE TIME DAILY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM,

OKLAHOMA, 2013.  Consumption of less

than one fruit a day 7/30/2015

| | was lowest in Stephens

County (65.7%).  Ottawa County had the highest rate (43.3%).  Only 17 counties had rates of fruit consumption higher than the state rate of 50.4% (48.9-51.8%,

95% CI). PopulationHealth Needs Assessment

35 Fruit consumption among Oklahomans was lowest among those who had income less than $15,000 (55.3%, 51.0- 59.5%, 95% CI) or income of $15,000-$24,999 (53.6%, 50.1-57.1%, 95% CI) and was highest among those with incomes $75,000 and more (46.0%, 42.8-49.2%, 95% CI). Females were more likely to eat at least one fruit daily (46.6%, 44.4-48.2%, 95% CI) compared to males (54.6, 52.3-56.9%, 95% CI). Those 65 years and older were more likely to consume fruit at least daily (40.5%, 38.3-42.8%, 95%CI) than other age groups.

Note: The Behavioral Risk Factor Surveillance System includes six questions about fruit and vegetable intake asked via a telephone survey, preceded by the following statement: "These next questions are about the fruits and vegetables you ate or drank during the last 30 days. Please think about all forms of fruits and vegetables including cooked or raw, fresh, frozen or canned. Please think about all meals, snacks, and food consumed at home and away from home." Respondents were asked to report consumption as times per day, week or month: 1) 100% PURE fruit juices: 2) fruit, including fresh, frozen, or canned fruit (not counting juice); 3) cooked or canned beans (not including long green beans); 4) dark green vegetables; 5) orange-colored vegetables; 6) other vegetables. Total daily fruit consumption was calculated based on responses to questions 1 and 2, and total daily vegetable consumption was based on questions 3-6.

Nationally, 22.4% (22.2-22.7%, 95% CI) of persons consumed vegetables less than one time daily in 2013. During the same period, 25.3% (23.9-26.7%, 95% CI) of Oklahomans consumed vegetables less than one time daily. This ranked Oklahoma among the bottom one-fifth of states for vegetable consumption.66

PERCENT OF ADULTS WHO ATE AT LEAST ONE VEGETABLE A DAY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA,

2013.  Vegetable consumption at least once daily was lowest in Hughes County with a rate of 45.0%.  Texas County had the best rate in the

state at 12.6%.  Nearly half of the counties (35, 45%)

had vegetable 7/30/2015 | | consumption at a higher rate than the state rate.

Vegetable consumption was lowest among Blacks (37.8%, 32.1-43.9%, 95% CI) and American Indians (31.4%, 25.6 -37.7%, 95% CI) not consuming at least one vegetable daily. Whites were more likely to consume at least a vegetable daily (24.0%, 22.5-25.6%, 95% CI).Those with the highest income (18.6%, 16.1-21.3%, 95% CI) were more likely to consume vegetables at least once daily compare to those with the incomes less than $34,999:

 36.9% (32.8-41.3%, 95% CI) among those with less than $15,000 income,  31.6% (28.3-35.1%, 95% CI) among those with $15,000-$24,999 income, and

 26.0% (22.0-30.4%, 95% CI) among those with $25,000-$34,999 income. PopulationHealth Needs Assessment

36 37 37

Less than one-quarter of females (22%, 20.6-24.0%, 95% CI) did not consume vegetables daily while nearly 30% of males did not consume vegetables daily (28.5%, 26.4-30.6%, 95%CI). Those who were 18-24 years of age reported less daily vegetable consumption (35.8%, 30.3-41.7%, 95% CI) than other age groups.

As with physical activity, there were other indicators of nutrition available for the state.

INDICATORS OF NUTRITION, OKLAHOMA, 2013.67 Data Indicator Data 95% CI Year Source Adults who consume fruit < 1 time daily 50.4% (48.9-51.8) 2013 1 Adults who consume vegetables < 1 time daily 25.3% (23.9-26.7) 2013 1 Adolescents who consume fruit < 1 time daily 48.3% (44.5-52.1) 2013 2 Adolescents who consume vegetables < 1 time daily 44.4% (41.4-47.4) 2013 2 Adolescents who drank soda daily 31.3% (27.6-35.1) 2013 2 Adolescents watching 3 or more hours of TV daily 31.9% (28.2-35.9) 2013 2 Date sources: 1=Behavioral Risk Factor Surveillance System, 2=Youth Risk Behavior Survey

Food Security

Food industry marketing, many fast food restaurants, and few grocery stores were identified as community factors that influence unhealthy food choices. In 2013, it was estimated that about 17% (654,640) of Oklahomans experienced a lack of access to food and uncertain availability of nutritiously adequate foods. For children, the situation is worse. Food insecurity among children is 26.0% (242,990 children). Nearly one-third (31%) are likely ineligible for federal nutrition programs (incomes above 185% of poverty).68

PERCENT OF POPULATION RESIDING IN A FOOD DESERT, U.S. DEPARTMENT OF AGRICULTURE – FOOD ATLAS, OKLAHOMA, 2013.  Food desert designations identify areas of low income and low food access.  More than one in five Oklahomans (21.1%) lived within a food desert.  Out of the 77 counties

7/30/2015 in Oklahoma, residents in | | 43 counties had to travel more than 10 miles to reach a full service grocery store in rural areas and more than a mile to a grocery store in urban areas.

There were several indicators that addressed access to food through policies or programs.

PopulationHealth Needs Assessment

37 ACCESS TO FOOD INDICATORS, OKLAHOMA. Data Indicator Data 95% CI Year Source

Census tracts with healthier food retailers 51.1% N/A 2011 1

2001- State-level policy for healthier food retail Yes N/A 1 2011

Farmers markets per 100,000 residents 1.9 N/A 2012 1

Farmers markets that accept SNAP benefits 14.1% N/A 2012 1

Farmers markets that accept WIC coupons N/A N/A 2012 1

Number of Local Food Policy Councils 1 N/A 2012 2

Middle/high schools offer fruits/vegetables at 27.6% N/A 2012 1 celebrations

Cropland harvested for fruits and vegetables 0.3% N/A 2007 1

State-level Food Policy Council Yes N/A 2012 2

State-level farm to school/preschool policy Yes N/A 2011 1

State child care regulations meet national standards: No N/A 2011 1 fruit

State child care regulations meet national standards: No N/A 2011 1 vegetable

Number of food hubs in each state 2 N/A 2012 1

States that authorize farmers to accept WIC CVV Yes N/A 2012 1

Data Sources: 1= State Indicator Report on Fruits and Vegetables and 2= Community Food Security Coalition. Oklahoma has several state-level policies that many other states do not have:  Oklahoma was one of 28 states with a state-level farm to school/pre-school policy (2011).

 Oklahoma was one of 27 states with a state-level Food Policy Council (2012). 7/30/2015

| |

 Oklahoma was among the 19 states that authorized farmers to accept WIC Cash Value Vouchers (2012).

Food insecurity is associated with increased risk of hypertension. This inverse relationship persists after controlling for socio-economic status, health insurance, marital status,

tobacco use, and demographics.69 PopulationHealth Needs Assessment

38 39 39

B. Clinical Care Overweight and obesity have been associated with increased risk of diabetes, hypertension, and other diseases.70,71 Those who were obese were more likely to have diabetes and prediabetes than those who were of normal weight. Persons who were overweight were more likely to have diabetes than those of normal weight; however, there was no statistical difference between the percentage of overweight and normal weight persons with prediabetes.

WEIGHT STATUS BY DIABETES DIAGNOSIS, OKLAHOMA BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.72 Weight Status Ever Told by Doctor You Have Diabetes Yes Yes, but No No, Pre- Female During Diabetes/Borderline Pregnancy Diabetes

Normal % 5.7% 0.4% 93.0% 0.9% 95% CI (4.5-6.9%) (0.2-0.7%) (91.7-94.3%) (0.6-1.2%) Overweight % 8.3% 0.4% 90.0% 1.3% 95% CI (7.2-9.3%) (0.2-0.6%) (88.8-91.2%) (0.8-1.8%) Obese % 19.7% 0.8% 77.0% 2.4% 95% CI (17.9-21.5%) (0.4-1.3%) (75.1-79.0%) (1.7-3.2%) Total % 11.2% 0.6% 86.8% 1.5%

95% CI (10.3-12.0%) (0.4-0.7%) (85.9-87.6%) (1.2-1.8%)

Those with normal weight were less likely to have reported having high blood pressure. The percentages of those who reported high blood pressure increased among those who were overweight and increased again for those who were obese.

WEIGHT STATUS BY HYPERTENSION DIAGNOSIS, OKLAHOMA BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA,

2013.73

Weight Status Reported High Blood Pressure

No Yes 7/30/2015

| | Normal % 74.7% 25.3%

95% CI (72.7-76.7%) (23.3-27.3%) Overweight % 63.5% 36.5% 95% CI (61.3-65.7%) (34.3-38.7%) Obese % 48.2% 51.8% 95% CI (45.7-50.8%) (49.2-54.3%) Total % 62.2% 37.8%

95% CI (60.8-63.5%) (36.5-39.2%)

PopulationHealth Needs Assessment

39 Among those with high blood pressure, those who were obese were more likely to be taking medication for hypertension compared to those of normal weight.

WEIGHT STATUS AND USE OF MEDICATION FOR HYPERTENSION, OKLAHOMA BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.74 Weight Taking Medicine for High Blood Pressure

Yes No

Normal % 74.6% 25.4% 95% CI (70.4-78.7%) (21.3-29.6%) Overweight % 78.8% 21.2% 95% CI (75.7-82.0%) (18.0-24.3%) Obese % 82.3% 17.7% 95% CI (79.6-85.0%) (15.0-20.4%) Total % 79.4% 20.6%

95% CI (77.6-81.3%) (18.7- 2.4%)

C. Health Care Cost In the United States, obesity-related medical treatment costs between $147 and $210 billion a year, equivalent to nearly 10% of yearly medical spending.75 76 Medicare and Medicaid were responsible for 42% of those costs. Moreover, obese people had 42% more in health care costs than healthy weight persons. Specific health care costs in Oklahoma were addressed within the Diabetes and Hypertension sections of the assessment.

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PopulationHealth Needs Assessment

40 41 41

6. Diabetes Diabetes includes a group of conditions in which the body does not produce and/or use insulin properly. Insulin is a hormone that is needed to store dietary sugar in a form that the body can utilize to obtain the energy it needs for daily life. Diabetes can have a wide range of short-term to long-term complications that may result in a hospitalization. Uncontrolled diabetes and short-term complications of diabetes can be life-threatening conditions, such as ketoacidosis, hyperosmolarity, and coma. Long-term complications from diabetes include kidney, nerve, and circulatory disorders. Diabetes is a risk factor for lower-limb amputation due to infection, nerve damage, and microvascular disease. High quality disease management and care has been shown to lead to reductions in almost all types of serious preventable hospitalizations.

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Diabetes Adult Diabetes 11.0% 8.7% 9% (2013) (2010) According to the 2014 National Diabetes Statistics Report, 29.1 million people (9.3% of the total population) had diabetes. Of those, more than one-quarter (27.8%) were undiagnosed, meaning eight million were unaware that they had diabetes. 77 Others suggest that one-third of those with Type 2 diabetes were undiagnosed.78

In Oklahoma, the number of adults with diabetes grew steadily during the past decade. Approximately 313,800 Oklahomans aged 18 years and older have been diagnosed with diabetes, according to the 2013 Behavioral Risk Factor Surveillance System.

TREND IN HEALTH METRIC.  The trend of adults with 12.0% 11.1% 11.5% 11.0% diabetes has remained steady 10.0% 8.0% over the past three years: 6.0%  In 2011, 11.1% (10.3- 4.0% 11.9%, 95% CI);

2.0%  In 2012, 11.5% (10.7- 0.0% 12.3%, 95% CI); and Adult Diabetes  In 2013, 11.0% (10.3-

2011 2012 2013 11.8%, 95% CI).

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Between 2005 and 2012, the death rate from diabetes decreased 38%. However, diabetes moved from the 7th leading cause of death in Oklahoma in 2012 to the 6th leading cause of death in 2013.

Diabetes is a major cause of renal failure, non- traumatic lower limb amputations, new cases of blindness, heart disease, and stroke.79

PopulationHealth Needs Assessment

41 A. Population Health Over all the rate of adult diabetes in Oklahoma was 11.0% in 2013. However, the rates varied by geographic area.

RATE OF ADULT DIABETES BY COUNTY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.

 Payne County had the lowest rate of adult diabetes (5.4%).  Pushmataha County had the highest rate of adult diabetes (20.4%).  The highest rates of diabetes were concentrated in southwest Oklahoma.  See the Appendix for 2013 rates by county.

While males experienced a higher rate of adult diabetes (53.5%, 49.8-57.2%, 95% CI), the rate was not statistically different than the rate among females (46.5%, 42.8-50.2%, 95% CI). American Indian adults were most likely to report having been told they had diabetes (15.7%), followed closely by Blacks (15.2%). The rates of gestational diabetes and prediabetes were not statistically different by race/ethnicity.

ADULT DIABETES BY RACE AND ETHNICITY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.80 Race/Ethnicity Ever Told by Doctor You Have Diabetes

Yes Yes, During No No, Pre- Pregnancy Diabetes/Borderline Diabetes White % 10.7% 0.6% 87.3% 1.4%

95% CI (9.8-11.6%) (0.4-0.8%) (86.3-88.2%) (1.1-1.7%) 7/30/2015 | | Black % 15.2% * 82.4% 1.8% 95% CI (11.5-18.9%) n/a (78.5-86.4%) (0.5-3.0%) American % 15.7% 0.9 81.5% 2.0% Indian 95% CI (12.1-19.2%) (0.1-1.7)% (77.5-85.5%) (0.2-3.7%) Other % 10.9% * 88.3% * 95% CI (3.0-18.8%) n/a (80.3-96.3%) n/a Multi- % 12.1% * 85.4% 2.3% cultural 95% CI (8.8-15.4%) n/a (81.8-89.1%) (0.6-4.0%) Hispanic % 6.4% 1.4% 91.3% * 95% CI (4.2-8.5%) (0.2-2.5%) (88.6-93.9%) n/a Total % 11.1% 0.7% 86.8% 1.5%

95% CI (10.3-11.9%) (0.5-0.9)% (85.9-87.6)% (1.2-1.8%) PopulationHealth Needs Assessment

42 43 43

The rate of adult diabetes was highest among Oklahomans aged 65 years and older (22.4%) with approximately one out of five seniors diagnosed with diabetes. The rate of diabetes increased with age:  18-34 year olds: 2.3%, 1.2-3.4%, 95% CI;  35-64 year olds: 12.1%, 10.9-13.3%, 95% CI; and  65 years and older: 22.4%, 20.3-24.3%, 95% CI.

The rates of prediabetes were not statistically different between the three age groups. Those who had less than $25,000 in household income had higher rates of diabetes compared to those with higher incomes:  < $15,000: 15.0% (12.6-17.4%, 95% CI);  $15,000-$24,999: 14.9% (12.5-17.2%, 95% CI);  $25,000-$49,000: 10.4% (9.0-11.8%, 95% CI);  $50,000-$74,999: 9.9% (7.9-11.9%, 95%CI); and  $75,000 +: 7.9% (6.3-9.4%, 95% CI).

The inverse relationship between increased income and decreased rate of diabetes was also present among those aged 65 years and older. Among all who reported a diabetes diagnosis, 55.2% reported taking a course on how to manage their diabetes. The rate was not statistically different by age, income, or race.

When adjusted for the age differences in the population, counties in western Oklahoma had higher rates of death due to diabetes. Three years of data were used to stabilize the rates; however, some distinctions were needed within the county rate map. Striped, unstable counties, regardless of color, had annual rates that can drastically vary due to a small number of deaths (< 20). Blue-colored, not reportable counties had too few deaths (<5) to report data due to confidentiality.

AGE-ADJUSTED DIABETES MORTALITY BY COUNTY, OKLAHOMA, 2011-2013.

 Adair

County had the highest stable rate of

diabetes-

7/30/2015 | |

related death (67.7%).  Washington County had the lowest stable rate of diabetes- related death (17.1%).

PopulationHealth Needs Assessment

43 B. Clinical Health Diabetes is a dangerous disease as it can lead to heart disease, blindness, and kidney failure.  Seventy-one percent (71.2%, 67.8-74.6%, 95% CI) of those with diabetes had also been told that they had high blood cholesterol compared to 36.1% (34.5-37.6%, 95% CI) of those who did not report diabetes.  Reported high blood pressure among those with diabetes (78.7%, 75.3-82.0%, 95% CI) was higher than high blood pressure among those without reported diabetes (31.9%, 30.6-33.3%, 95% CI).  Among those who reported diabetes, 51% also had a retinal eye exam within the past 12 months. The rates by gender, race/ethnicity, income, or insulin status were not statistically different.  The rates of retinal exam were not statistically different between those who had previously been told that diabetes had affected their eyes or had retinopathy (57.3%, 45.9-68.7%, 95% CI) and those who had not been told (51.1%, 45.6-56.6%, 95% CI).  Those with diabetes also had kidney disease (8.5%, 6.6-10.3%, 95% CI) at a higher rate than those who did not have diabetes (2.6%, 2.1-3.1%, 95% CI).

Among those who reported being told by a doctor they had diabetes:  Half (50.7%, 45.8-55.7%, 95% CI) were between 45-54 years of age at the time of diagnosis.  Nearly one-third (31.3%, 26.4-36.1%, 95% CI) were 20 to 44 years of age.  Fewer were diagnosed at 65 years and older (15.6%, 12.6-18.6%, 95% CI) and before the age of 20 (2.3%, 0.7-4.0%, 95% CI). Among the population that reported being told by a doctor they had diabetes, nearly one- third (31.9%) used insulin. The rate of insulin used varied by age group.

The percentage of those who used insulin varied by age group. Additionally, American Indians who had diabetes diagnosis had a high rate of current smoking (30.2%).

INSULIN USE BY AGE GROUP, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.

Age Groups Insulin Taking Status

Yes No 18-34 years % * * CI n/a n/a

35-64 years % 34.1% 65.9% CI (27.4-40.8%) (59.2-72.6%) 65 years or older % 28.4% 71.6% CI (22.6-34.2%) (65.8-77.4%)

Total % 31.9% 68.1% 7/30/2015 | | CI (27.3-36.5%) (63.5-72.7%)

AMERICAN INDIANS WITH DIABETES DIAGNOSIS AND CURRENT SMOKER, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013. Told by Doctor You Have Diabetes Currently Smoking No Yes American Indian % 69.8% 30.2% 95% CI (58.3-81.2%) (18.8-41.7%)

Nearly 8 out of 10 who reported diabetes also reported being told be a doctor they had hypertension (78.7%, 75.3- 82.0%, 95% CI). Similarly, 8 out of 10 American Indians with diabetes also reported having hypertension (85.5%,

76.6-94.5%, 95% CI). PopulationHealth Needs Assessment

44 45 45

C. Health Care Costs While most of those who reported having diabetes had insurance (91%), 17% reported delaying medical care because of the cost. Individual risk factors associated with diabetes and higher hospital admissions include:  Overweight  Lower socio-economic status One study found that two-thirds of admissions were due to cessation of insulin therapy - over half of the time for financial or other difficulties obtaining insulin.  Access to care A study found that uninsured patients had more than 2 times the risk of admission for diabetic ketoacidosis and coma than privately insured patients.  Low HDL cholesterol or high triglycerides, high blood pressure  Duration of disease  Family history of diabetes  Women who had gestational diabetes  Age  Race Minorities (American Indians, Blacks, and Hispanics) have higher rates of diabetes, experience poorer glycemic control and more complications. It is unclear whether poor glycemic control arises from poor quality medical care, the non-compliance of patients, a lack of education, or a lack of access to care.

Population risk factors associated with diabetes and higher hospital admissions include:  Access to care Studies have found that areas with high rates of diabetes admissions also tend to have high rates of admissions for other disease conditions that require ongoing self-management and medical care. Areas with self- reported low access to care also reported higher hospital admissions for diabetes.  Race Minorities have higher rates of diabetes. Areas with higher minority populations may have higher rates of

hospitalization.  Systemic bias Administrative coding by health facilities in certain areas may create a bias for hospitalization rates for

diabetes.

7/30/2015 | | In 2013, there were 6,980 inpatient hospital discharges with diabetes mellitus as the principle diagnosis. The total charges were nearly $222 million and averaged $31,776.09 per discharge. The inpatient stays accounted for a total of 35,810 days and averaged 5.13 days in length. The majority of the discharge status were discharged to home (67.5%), followed by discharged to home under care of home health (12.7%) or skilled nursing facility (5.7%). Those with Medicare had the most discharges.

DIABETES MELLITUS HOSPITAL DISCHARGES BY PAYER GROUP, OKLAHOMA, 2013.81 Payer Group Discharges Total Length of Average Length of Total Charges Average Stay (days) Stay (days) Charges Commercial 1,313 5,542 4.22 $35,424,759.00 $26,980.01 Medicare 2,921 18,841 6.45 $113,424,918.00 $38,830.85 Medicaid 1,353 5,636 4.17 $36,138,233.00 $26,709.71

VA/Military 80 347 4.34 $2,432,747.00 $30,409.34 PopulationHealth Needs Assessment

45 Payer Group Discharges Total Length of Average Length of Total Charges Average Stay (days) Stay (days) Charges Workers Comp. * * * * * Uninsured/Self-pay 1,104 4,390 3.98 $28,959,653.00 $26,231.57 Other payers 200 977 4.89 $5,096,691.00 $25,483.46 * Calculations suppressed due to small number of discharges (10 or Less)

Preventable Hospitalizations Three categories of preventable hospitalizations were included in the assessment.

 Diabetes Short-Term Complication Admissions - short-term complications of diabetes mellitus include diabetic ketoacidosis, hyperosmolarity, and coma. These life-threatening emergencies arise when a patient experiences an excess of glucose (hyperglycemia) or insulin (hypoglycemia). Proper outpatient treatment and adherence to care may reduce the incidence of diabetic short-term complications, and lower rates represent better quality care.  Diabetes Long-Term Complication Admission - long-term complications of diabetes mellitus include renal, eye, neurological, and circulatory disorders. Long-term complications occur at some time in the majority of patients with diabetes to some degree. Proper outpatient treatment and adherence to care may reduce the incidence of diabetic long-term complications, and lower rates represent better quality care.  Lower-Extremity Amputation among Patients with Diabetes - diabetes is a major risk factor for lower- extremity amputation, which can be caused by infection, neuropathy, and microvascular disease. Proper and continued treatment and glucose control may reduce the incidence of lower-extremity amputation, and lower rates represent better quality care.

DIABETES-RELATED PREVENTABLE HOSPITALIZATIONS BY CONDITION, MONAHRQ®, OKLAHOMA, 2013. Cost Savings by % Reduction in Number of Stay # of Condition 10% 20% 30% 40% 50% Stays Short-term

complications 2,214 $1,875,913 $3,751,826 $5,627,739 $7,503,652 $9,379,565

Long-term complication 2,492 $3,349,496 $6,698,992 $10,048,488 $13,397,984 $16,747,480

Lower limb 7/30/2015 | | amputation 299 $874,978 $1,749,958 $2,624,937 $3,499,916 $4,374,895 *Cost savings are based on charges that have been adjusted to costs, using hospital-specific cost-to-charge ratios. Values based on 10 (numerator suppression value) or fewer discharges are suppressed to protect confidentiality of patients and are not included within the stays or cost reductions.

PopulationHealth Needs Assessment

46 47 47

7. Hypertension Hypertension or high blood pressure is a medical condition that occurs when excess force is exerted on the vessel walls as blood moves through the body. This condition is often without symptoms and has multiple causes. High blood pressure increases the risk for heart disease and stroke. High blood pressure is often controllable with appropriate use of drug therapy, medical care, and self-management. Having certain medical conditions, like diabetes or prehypertension, can increase your chances of developing high blood pressure. Unhealthy behaviors such as smoking, eating foods high in sodium and low in potassium, not getting enough physical activity, being obese, and drinking too much alcohol can also increase your risk for high blood pressure.

Also known as high blood pressure, hypertension can lead to heart attack, stroke, heart failure, and kidney disease.83

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Hypertension Adult Hypertension 37.5% (2013) 31.4% (2013) 36% Heart Disease Deaths 9,703 (2013) n.a. 8,204

The 2011-2012 National Health and Nutrition Examination Survey (NHANES) found one in three U.S. adults (71 million people) had hypertension.84 Among adults with hypertension, 82.7% were aware of their hypertension, 75.6% reported currently taking prescribed medication to lower their blood pressure, and 51.8% had their blood pressure controlled.85 Nearly half of those with hypertension (48.2%) did not have their condition under control.86 Among those with uncontrolled hypertension, 36.2% were not aware that they had hypertension. 87 Of the 35.8 million U.S. adults with uncontrolled hypertension, 89.4% reported having a usual source of health care and

85.2% reported having health insurance.88

TRENDS IN HEALTH METRICS.  The prevalence of hypertension in 2011 of 35.5% 40.0% 35.5% 37.5% (34.2-36.8%, 95% CI) was not statistically different

7/30/2015 than the 2013 prevalence of 37.5% (36.2-38.8%, 95% 20.0% | | CI). 0.0% Hypertension Prevalence 2011 2012 2013

 While the number of heart disease-related deaths has 15,000 increased from 2011 to 2013, the age-adjusted death 9,434 9,173 9,703 10,000 rate decreased from 229.7 per 100,000 to 228.0 per 5,000 100,000 population during the same period. 0 2011 2012 2013

PopulationHealth Needs Assessment Number of Heart Disease Deaths

47 A. Population Health More than one-third of Oklahomans aged 18 years and older had hypertension in 2013. However, the rates varied by geographic area. As of 2013, more than half of the population 18 years and older had hypertension in Bryan, Marshall, Greer, Jefferson, McIntosh, and Pushmataha counties.

RATE OF HYPERTENSION BY COUNTY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.  Jackson County had the lowest rate of hypertension (29.0%).  Pushmataha County had the highest rate (64.3%).  Only 18 counties had a rate lower than the state rate (37.5%).

Those who reported their race as Black were more likely to have reported having high blood pressure compared to the general population and all race/ethnicity populations.

HYPERTENSION BY RACE, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013. 89 Race/Ethnicity Reported High Blood Pressure No Yes White % 61.6% 38.4% CI (60.1-63.0%) (37.0-39.9%) Black % 51.5% 48.5%

CI (45.8-57.2%) (42.8-54.2%) American Indian % 61.5% 38.5% CI (56.1-66.9%) (33.1-43.9%) Other % 77.3% 22.7%

CI (67.3-87.3%) (12.7-32.7%) 7/30/2015 | | Multi-cultural % 60.5% 39.5% CI (54.6-66.4%) (33.6-45.4%) Hispanic % 77.4% 22.6% CI (73.0-81.8%) (18.2-27.0%) Total % 62.4% 37.6% CI (61.1-63.7%) (36.3-38.9%)

Males (39.5%, 37.5-41.6%, 95% CI) were slightly more likely to report high blood pressure than females (35.6%, 34.0-37.3%, 95%CI); however, the rates were not statistically different. Those with incomes of $75,000 or more (32.1%, 29.3-34.9%, 95%CI) were less likely to report having high blood pressure than all income categories. Those aged 65 and older were more likely to report having high blood pressure (65.6%, 63.5-67.7%, 95% CI) compared to

those aged 35-64 years (41.6%, 39.8-43.4%, 95% CI) and those aged 18-34 years (14%, 11.8-16.1%, 95% CI). PopulationHealth Needs Assessment

48 49 49

B. Clinical Care A blend of prescribed medication and lifestyle changes, such as dietary changes and tobacco cessation have been shown to control hypertension.90

Nearly 80% (79.1%, 77.2-80.9%, 95% CI) of those who reported being told they had high blood pressure also reported taking medicine to control the hypertension. Among those who reported taking medicine for high blood pressure, more than one-quarter (26.5%, 24.6-28.4%, 95% CI) also reported being told they had diabetes.

The percentages of adult current smokers were not statistically different from the total population and those who were taking medications to control hypertension. However, the rate of current smoking was higher among those with a hypertension diagnosis and not taking medication (32.9%, 27.9-38.0%, 95% CI) compared to the total population 23.7% (22.4-25.0%, 95% CI). The percentages of current smokers among those who reported their race as Black were not statistically different among those who took medication for high blood pressure and the general population.

PERCENTAGE OF CURRENT SMOKERS AMONG THOSE WITH HYPERTENSION BY MEDICATION STATUS, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013. Taking Medicine For High Blood Pressure Currently Smoking No Yes Yes % 79.1% 20.9% 95% CI (77.1-81.0%) (19.0-22.9%) No % 67.1% 32.9% 95% CI (62.0-72.1%) (27.9-38.0%)

Among the general population who reported they were advised by a doctor to reduce sodium/salt intake, 79.5% (76.2-82.8%, 95% CI) reported they watched their salt intake. Less than half (44.6%, 42.2-47.1%, 95% CI) of those who reported never being advised to reduce salt said they watched their salt intake.

Nearly a quarter (23.6%) of the population reported that they had been advised by a doctor to reduce sodium/salt intake. Those who had high blood pressure were four times more likely to report they had been told by a doctor to reduce their sodium/salt intake.

PERCENTAGE ADVISED TO REDUCE SODIUM/SALT INTAKE AMONG THOSE WITH HYPERTENSION DIAGNOSIS STATUS, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013. Reported High Blood Pressure Ever Advised by Doctor to Reduce Sodium/Salt Intake

7/30/2015 Yes No | | No % 11.5% 88.5% 95% CI (9.8-13.1%) (86.9-90.2%) Yes % 44.2% 55.8% 95% CI (41.3-47.2%) (52.8-58.7%)

Of those who reported high blood pressure, 44.4% (42.3-46.5%, 95% CI) were also obese. The obesity rate among those who did not report high blood pressure was 25.2% (23.5-27.0%, 95% CI). However, the rates of obesity among those with high blood pressure were nearly identical among the total population and those who reported their race as Black.

PopulationHealth Needs Assessment

49 C. Health Care Costs Individual risk factors associated with high blood pressure and higher hospital admissions include: 91  Smoking  Access to care Studies have found that self-rated access to care explained nearly one-quarter of hospital admissions for hypertension.

 Sodium and salt intake  Medication adherence  Alcohol consumption  Diabetes  Gender  Age

Population risk factors associated with hypertension and higher hospital admissions included: 92  Lower socio-economic areas Low-income zip codes had nearly 8 times more hypertension- related hospitalizations than high-income zip codes.

HOSPITAL DISCHARGES FOR HYPERTENSION WITH AND WITHOUT MAJOR COMPLICATION OR CO-MORBIDITY, OKLAHOMA, 2013.93 Payer Group Discharges Total Length Average Length Total Average of Stay (Days) of Stay (Days) Charges Charges Commercial 290 651 2.24 $4,879,814.00 $16,826.94 Medicare 566 1,513 2.67 $9,119,619.00 $16,112.40 Medicaid 142 379 2.67 $2,354,015.00 $16,577.57 Veterans 19 47 2.47 $424,322.00 $22,332.74 Affairs/Military Uninsured/Self-pay 194 388 2.00 $2,997,680.00 $15,451.96

Other payers 23 71 3.09 $440,640.00 $19,158.26

Preventable Hospitalizations

Hypertension is a chronic condition that is often controllable in an outpatient setting with appropriate use of drug

therapy. Proper outpatient treatment may reduce admissions for hypertension, and lower rates represent better 7/30/2015

| | quality care. There were an estimated 1,275high blood pressure-related preventable hospitalizations in Oklahoma during 2013.

HIGH BLOOD PRESSURE-RELATED PREVENTABLE HOSPITALIZATIONS BY CONDITION, MONAHRQ®, OKLAHOMA, 2013. # of Condition 10% 20% 30% 40% 50% Stays High Blood Pressure 1,275 $911,428 $1,822,857 $2,734,286 $3,645,714 $4,557,143 *Cost savings are based on charges that have been adjusted to costs, using hospital-specific cost-to-charge ratios. Values based on 10 (numerator suppression value) or fewer discharges are suppressed to protect confidentiality of patients and are not included within the stays or cost reductions.

PopulationHealth Needs Assessment

50 51 51

8. Behavioral Health Mental health and substance abuse issues are among the pressing health concerns facing Oklahomans. In the past year, 21.9% of adults reported having a mental health issue and 12% reported experiencing a substance abuse issue, representing 700,000 to 950,000 Oklahomans living with diseases of the brain.94 The life expectancy for people with untreated behavioral health diseases is significantly less than the general population, upwards of 25-30 years.95

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Behavioral Health

Untreated Mental 86% treatment gap n.a. 76% gap Illness (2012-13) 8.81% 8.66% 7.8% Addiction Disorders (2012-13) (2012-13) Suicide Deaths 22.0 per 100,000 16.5 per 100,000 19.4 per 100,000 18years and older (2013) (2013)

TRENDS IN HEALTH METRICS GAP IN THE PUBLIC SERVICE SECTOR, ANY MENTAL ILLNESS IN THE PAST YEAR, NATIONAL SURVEY ON DRUG USE AND HEALTH, OKLAHOMA, 2011-2013. 100% 89% 86%

50%

0% Untreated Mental Illness

2011-12 2012-13

DEPENDENCE OR ABUSE OF ILLICIT DRUGS OR ALCOHOL IN THE PAST YEAR, NATIONAL SURVEY ON DRUG USE AND HEALTH, OKLAHOMA, 2011-2013. 20.00% 9.94% 8.81% 10.00%

0.00% Addiction Disorders

7/30/2015

| | 2011-12 2012-13

SUICIDE RATE PER 100,000 AMONG THOSE 18 YEARS AND OLDER, VITAL STATISTICS, OKLAHOMA, 2013. 25.0 23.7 22.2 22.0 20.0 15.0 10.0 5.0 0.0 Suicides

2011 2012 2013 PopulationHealth Needs Assessment

51 A. Population Health Results from the 2013 National Survey on Drug Use and Health, 9.4 % of Americans aged 12 years and older (estimated 24.6 million) had used during the month an illicit drug, such as marijuana, cocaine, heroin, hallucinogens, inhalants, or prescription-type psychotherapeutics used non-medically. 96 Eighty percent of those had used marijuana. Nearly one quarter (22.9%) of persons aged 12 years and older were binge alcohol users. This translates to about 60.1 million Americans.

Behavioral health conditions increase the risk for communicable and chronic diseases, unintentional and intentional injury, and shorter life expectancy.97

Results from the administration of the 2011-2012 and 2012-2013 National Survey on Drug Use and Health shows that Oklahomans continue to experience mental illness or dependence on illicit drugs and alcohol. The findings also highlight a continued gap in public services.

NATIONAL AND STATE BEHAVIORAL HEALTH INDICATORS, NATIONAL SURVEY ON DRUG USE AND HEALTH, 2011-2013.98 Measure Area Percentage Gap in the Percentage Gap in the Public Public Service Service Sector Sector 2012-2013 2011-2012 Dependence or abuse of illicit National 8.66 8.46 drugs or alcohol in the past Oklahoma 8.81 -87% 9.94 -89% year - Table 20

Serious mental illness in the National 4.14 3.98 past year – Table 23 Oklahoma 5.14 -67% 5.42 -67% Any mental illness in the past National 18.36 18.04 year – Table 24 Oklahoma 20.55 -86% 22.21 -89%

Needing but not receiving National 2.31 2.21 7/30/2015 | | treatment for illicit drug use in Oklahoma 1.83 1.99 the past year – Table 21 Needing but not receiving National 6.76 6.62 treatment for alcohol use in Oklahoma 7.07 8.45 the past year – Table 22

According to the 2013 Behavioral Risk Factor Surveillance System, 17.7% (15.9-19.6%, 95% CI) of Oklahoman males reported drinking five or more alcoholic drinks on one occasion. Additionally, 7.9% (6.8-9.0%, 95% CI) of females reported having four of more alcoholic drinks on one occasion. Oklahomans also reported using prescription

drugs without having a prescription. The percentage of those reporting non-prescription use of prescription drugs PopulationHealth Needs Assessment

52 53 53

varied by educational status. Those with some college or college graduates reported were more likely to report using prescription drugs without prescription four or more times in the past 30 days.

PRESCRIPTION DRUG INTAKE WITHOUT PRESCRIPTION BY EDUCATION, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013. Education Levels Prescription Drug Intake W/O Prescription In The Past 30 Days 0 Times 1 To 3 Times 4 Or More Times Less than HS % 81.2 * 8.1 95% CI (68.1-94.3%) n/a (0.0-16.3%) High School % 74.1 17.9 8.0 95% CI (64.9-83.3%) (9.5-26.3%) (3.1-13.0%) Some College % 78.6 10.7 10.6 95% CI (69.5-87.8%) (2.9-18.5%) (4.6-16.7%) College % 67.6 14.7 17.7 95% CI (55.9-79.4%) (5.9-23.5%) (7.7-27.6%) Total % 75.9 13.7 10.4 95% CI (70.7-81.2%) (9.2-18.2%) (7.0-13.7%)

In 2013, Oklahoma had the sixth highest unintentional poisoning mortality rate in the United States.99 Unintentional poisoning is the leading cause of injury death for Oklahomans aged 25-64. Poisonings can be caused by many substances such as prescription drugs, illegal drugs, alcohol, carbon monoxide, chemicals, gases, and even water. The pattern of drug over dose deaths has shifted from illicit drugs (heroin, cocaine, methamphetamines, etc.) to prescription drugs during the late 1990’s. In 1999, 127 Oklahomans dies of an unintentional poisoning: by 2010, 662 Oklahomans had died from unintentional poisoning.100 In a five year period (2007-2011), 81% of the 3,200 unintentional poisoning deaths involved at least one prescription drug.101 Oklahoma also leads the nation in prescription painkiller sales per capita.102

The 2013 Behavioral Risk Factor Surveillance System Survey results showed that nearly one-quarter (23.4%, 22.2- 24.6%) of Oklahomans reported ever having a depressive disorder.

DEPRESSIVE DISORDER EVER BY RACE/ETHNICITY, BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM, OKLAHOMA, 2013.

Race/Ethnicity Ever Have Depressive Disorder

Yes No

White % 24.1 75.9 7/30/2015

| | 95% CI (22.7-25.5%) (74.5-77.3%)

Black % 17.7 82.3 95% CI (13.4-22.0%) (78.0-86.6%) American % 25.6 74.4 Indian 95% CI (20.0-31.1%) (68.9-80.0%) Other % 11.5 88.5 95% CI (3.5-19.4%) (80.6-96.5%) Multi-Cultural % 31.1 68.9 95% CI (25.4-36.9%) (63.1-74.6%) Hispanic % 19.2 80.8 95% CI (14.7-23.7%) (76.3-85.3%) Total % 23.4 76.6

PopulationHealth Needs Assessment 95% CI (22.2-24.6%) (75.4-77.8%)

53 Oklahoma is one of 18 states that participate in the National Violent Death Reporting System. The Oklahoma Violent Death Reporting System (OK-VDRS) collects data on homicides, suicides, deaths due to unintentional firearm injury, legal intervention, or terrorism. Deaths of undetermined manner/intent are also included in the reporting system. Suicide was the most common manner of violent death accounting for 3,836 deaths during 2004 to 2010, or an average of 548 deaths per year (average annual rate = 15.1 per 100,000 population).103 The rate of suicide increased from 14.1 per 100,000 population in 2004 to 16.9 per 100,000 population in 2010. From 2004-2010, 79% of suicide victims were male and 21% were female. In 2% (86) of the suicide deaths, victims killed at least one other person before taking their own life resulting in 102 homicides. 104 Males 85 years of age and older had the highest suicide rate among all age groups. Among females, those aged 35-54 years were at greatest risk for suicide. White males and American Indian males had the highest rates of suicide, 25.5 and 20.1 per 100,000, respectively. Firearms were used in 60% of the suicide deaths, hanging/ strangulation in 19%, poisoning in 16%, and other/unknown methods were used in 5% of suicides. A substantial number of suicides were associated with a current depressed mood, intimate partner problem, mental health problem, physical health problem, or crisis in the past two weeks. Circumstances associated with suicide varied by age. Physical health problems were more often associated with suicide among persons 65 years and older while intimate partner problems were more often associated with suicides of persons less than 65 years of age. Almost 20% of the suicide victims had a history of suicide attempts. Lastly, nearly 25% of suicide victims had served in the U.S. Armed Forces. 105

AGE-ADJUSTED SUICIDE RATES PER 100,000 POPULATION, 18 YEARS AND OLDER, OKLAHOMA, 2011-2013.  The highest stable rate of suicide was in Beckham County (36.6).  The lowest stable rate was in Payne

County (11.7).  Cimarron and Harmon

counties had no

7/30/2015 | |

reported suicides.

Additional information on potential contributors and behavioral risk factors for children and adults are included

within the County Health Rankings maps located in the Overall Health section and data table in the Appendix. PopulationHealth Needs Assessment

54 55 55

B. Clinical Care In 2010, 10,374 eligible facilities responded to the National Mental Health Services Survey (N-MHSS) and reported a 1-day census of 3,186,636 clients enrolled in mental health treatment.106 More than two-thirds of facilities were operated by private nonprofit entities, about 10% were operated by private for-profit entities, and the remaining were operated by federal, state, local, or tribal governments. The majority (60.8%) of mental health facilities were outpatient or day treatment or partial hospitalization facilities. Of the facilities, 88% accepted Medicaid, 84% accepted client/patient fees, and 78.4% accepted private insurance. Approximately 70% of the facilities also accepted

Medicare and state mental health agency funds.107

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55 C. Health Costs

Hospitalizations Involving Mental and Substance Use Disorders (M/SUDs)

Among Adults, U.S., 2012 Nationally, nearly 1.8 million inpatient stays were primarily for M/SUDs (6.7 % of all stays). Compared with non-M/SUD stays, on average M/SUD stays were longer (6.6 days vs. 4.8 days for non-M/SUD stays) but had lower total costs ($6,300 vs. $12,600). Longer stays for M/SUD diagnoses were attributable primarily to stays for MD diagnoses (8.3 days). Mood disorders was the most common primary MD diagnosis (741,950 stays), and alcohol- related disorders was the most common SUD diagnosis (335,790 stays). Among stays with a primary MD or SUD diagnosis, 14% lacked insurance, more than two times greater than among stays without M/SUD diagnoses (6%). Public government payers covered more than half (56%) of all inpatient stays with a primary MD or SUD, Medicare was the most common payer for stays involving MD diagnoses only (37%). Medicaid was the most common payer for SUD diagnoses only (29%).

HCUP Statistical Brief # 191, June 2015

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9. Children’s Health Oklahoma ranks poorly for many key indicators of maternal and child health which will have long-term consequences for the state’s health.

METRIC OKLAHOMA UNITED STATES 2020 STATE TARGET

Children’s Health

6.8 per 1,000 live births 6.0 per 1000 live births 6.4 per 1,000 live births Infant Mortality (2013) (2013) 29.9 per 100,000 live 17.8 per 100,000 live 26.2 per 100,000 live Maternal Mortality births births (2011) births All ages, 1 yr. rate (2013) Injury Deaths Among 14.4 per 100,000 7.4 per 100,000 (2013) 13.9 per 100,000 0-17 years (2013)

TRENDS IN HEALTH METRICS INFANT MORTALITY RATE PER 1,000 LIVE BIRTHS, OKLAHOMA, 2011-2013.

8.0 7.2 7.5 6.8 6.0 4.0 2.0

0.0 Infant Mortaility

2011 2012 2013

MATERNAL MORTALITY RATE PER 100,000 LIVE BIRTHS, OKLAHOMA, 2011-2013. 40.0 34.4 29.9 30.0

20.0 13.3 10.0 0.0

Maternal Mortaility

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2011 2012 2013

UNINTENTIONAL INJURY MORTALITY RATE PER 100,000 AMONG PERSONS AGED 0-17 YEARS, OKLAHOMA, 2011-2013. 20.0 14.0 14.4 15.0 10.1 10.0 5.0 0.0 Unintentional Injury < 18 years

2011 2012 2013

PopulationHealth Needs Assessment

57 A. Population Health According to the Bureau of Health Resources and Services Administration (HRSA), the life course approach to conceptualizing healthcare needs and services evolved from research documenting the important role early life events play in shaping an individual’s health trajectory. Research suggests that birth outcomes in the United States appear to act less as indicators of poor healthcare and health behaviors, and more as indicators of deeper disparities among women of different social classes and ethnicities.108,109

Preconception Health and Racial Disparities, 2009-2011. PRAMS gram, June 2015 in press.

 Significant differences in preconception health persisted across racial and ethnic groups.  Non-Hispanic American Indian mothers had the highest rates of smoking three months before pregnancy (46.0%) compared to non-Hispanic Black (27.9%), White (37.3%) and Hispanic (12.1%) mothers.  Non-Hispanic White mothers reported the highest rates of alcohol use before pregnancy (57.3%) compared to all other mothers.  Pre-pregnancy folic acid/multivitamin use was lowest among the non-Hispanic Black and non-Hispanic American Indian mothers (27.9% and 27.3%, respectively).  Non-Hispanic Black mothers had significantly lower odds of smoking or drinking before pregnancy, when compared to white mothers, when controlling for demographic characteristics.  Non-Hispanic Black, Non-Hispanic American Indian, and Hispanic mothers were more likely to have been treated or checked for diabetes prior to pregnancy when compared to white mothers.  Non-Hispanic Black mothers were twice as likely as white mothers to have been checked or treated for high blood pressure prior to pregnancy.

SELECTED MATERNAL BEHAVIORS PRIOR TO PREGNANCY BY MATERNAL RACE/ETHNICITY, PREGNANCY RISK ASSESSMENT

MONITORING SYSTEM, OKLAHOMA 2009-2011.110

100 90 80 70 57.3 60 48.6 7/30/2015

46.0 48.2 |

40.7 50 37.3 38.8 35.7 40 27.9 27.7 27.3 27.3 30.6 30 27.9

Percent 20 12.1 10 0 Smoking 3 months before Drinking 3 months before Taking a Vitamin/Folic acid pregnancy pregnancy Selected Behaviors NH White NH Black NH American Indian NH Other Hispanic

Therefore, the infant mortality rate (IMR) is an important indicator of the health of the state and associated with maternal health, quality of and access to health care, community services, and socioeconomic conditions.111 The leading causes of infant PopulationHealth Needs Assessment

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death included congenital malformations (i.e., medical conditions present at birth), disorders related to short gestation (fewer than 37 weeks of pregnancy completed), low birth weight (less than 5 lbs., 8 oz.), and Sudden Infant Death Syndrome (SIDS).112

Five years of data were used to stabilize the rates; however, some distinctions were needed within the county rate map. Striped, unstable counties, regardless of color, had annual rates that drastically varied due to a small number of deaths (< 20). Blue-colored, not reportable counties had too few deaths (<5) to report data due to confidentiality.

INFANT MORTALITY RATE BY COUNTY, OKLAHOMA, 2009-2013.  Adair County had the highest stable rate at 12.6 infant deaths per 1,000 live births.  Several counties experienced no infant deaths from 2009-2013.  In 2013, the infant mortality rate in Oklahoma was 6.8, the lowest rate on record for Oklahoma infant deaths.

Preterm birth-related causes of death together accounted for 35% of all infant deaths in the U.S. during 2010.113 Congenital malformations, short gestation and disorders related to low birthweight, and prematurity were the three leading causes for infant mortality in Oklahoma.114 Rates of infant mortality are almost twice as high for African American infants and 1.5 times as high for American Indian infants, when compared to white infants. Preterm birth is a birth that occurs 3 weeks or more before a baby’s due date, in other words any baby born at less than 37 weeks is considered preterm. Preterm babies have less time to develop in the womb which puts them at risk for medical and developmental problems. Women with uterine or cervical abnormalities, a history of preterm birth, or carrying

7/30/2015 multiple babies (twins or more) are at the greatest risk for having a preterm birth. Other risk factors include smoking | | cigarettes, alcohol use, illicit drug use, and chronic health problems, such as diabetes, high blood pressure. Almost half of women who have preterm labor and birth have no known risk factors. Pre-term births decreased from 13.5% in 2007 to 13.0% in 2012, equivalent to 567 fewer pre-term infants.

Maternal mortality has been viewed as an indicator of the overall effectiveness of the obstetrical and the general health care systems. Through appropriate interventions, prevention of risks, and reduction of racial disparities, these mortality rates can be dramatically decreased. Due to the relatively low number of maternal deaths per county, stable rates could not be achieved using five years of mortality data. The variation in maternal morality by year also results in an unstable state ratio that can be stabilized through the use of averaging multiple years of data.

PopulationHealth Needs Assessment

59 MATERNAL MORTALITY RATIO PER 100,000 LIVE BIRTHS, ALL AGES BY SINGLE AND MULTI-YEAR ROLLING AVERAGES, OKLAHOMA, 2004-2013. Year of Birth 1-Year 2-Year 3- Year 5-Year 2004 25.33 2005 40.53 32.97 29.27 2006 22.21 31.18 26.72 26.594 2007 18.16 20.16 22.579 31.08 2008 27.38 22.75 31.02 30.18 2009 47.66 37.49 36.90 32.60 2010 35.68 41.74 39.35 31.76 2011 34.43 35.067 27.80 32.30 2012 13.26 23.80 25.88 2013 29.97 21.67

CDC WONDER (NCHS Def: A34, O00-O95, O98-O99)

Nearly half (48.5%) of the unintentional injury deaths among children 0-17 years of age were motor vehicle-related. The next leading cause of death was drowning at 16.9%. Male children aged 0-17 years (16.50/ 100,000) were more likely to die from an unintentional injury than females of the same age group (12.12 per100,000). Unintentional injury deaths were relatively few, so multiple years of data were used to create a county level map. Five years of data were used to stabilize the rates; however, some distinctions were needed within the county rate map. Striped, unstable counties, regardless of color, had annual rates that drastically varied due to small number of deaths (< 20). Blue-colored, not reportable counties had too few deaths (<5) to report data due to confidentiality.

UNINTENTIONAL INJURY MORTALITY RATE PER 100,000, CHILDREN AGED 0-17 YEARS BY COUNTY, OKLAHOMA, 2009-2013.

 Several counties reported zero deaths from

2009-2013.  Tulsa County had

the highest 7/30/2015 | | stable rate at 10.8.

American Indian children had the highest rate of unintentional injury death and White children had the highest number of deaths. Teenagers (32.3) followed by children less than five years of age (19.6) had the highest rates of unintentional injury death among the four age groups.

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UNINTENTIONAL INJURY DEATHS AND RATES PER 100,000 BY RACE AMONG CHILDREN AGED 0-17 YEARS, OKLAHOMA, 2013. Race Number of Population Crude Deaths Rate White 91 681,894 13.35 Black 12* 107,820 11.13* Am Indian/AK Native 32 134,146 23.85 Asian/Pac Islander --- 23,167 --- * Rates based on 20 or fewer deaths may be unstable. Use with caution.

UNINTENTIONAL INJURY DEATHS AND RATES PER 100,000 BY RACE AMONG CHILDREN AGED 1-19 YEARS, OKLAHOMA, 2013. Age Group Number of Population Crude in Years Deaths Rate 00-04 52 264,479 19.66 05-09 28 268,844 10.41 10-14 14* 261,248 5.36* 15-19 83 256,391 32.37 Total 177 1,050,962 16.84

* Rates based on 20 or fewer deaths may be unstable. Use with caution.

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PopulationHealth Needs Assessment

61 B. Clinical Care Having no health insurance coverage or experiencing gaps or shifts in coverage were identified as barriers to receiving preventive health services and treatment for health problems that could affect pregnancy and newborn health. One study showed that private insurance coverage declined from the month prior to pregnancy to the time periods during pregnancy to at delivery. This decrease was not statistically different. However, the rates of uninsured and Medicaid coverage shifted dramatically between the periods of the month prior to pregnancy to during pregnancy.

PREVALENCE OF WOMEN WITH PRIVATE HEALTH INSURANCE COVERAGE, MEDICAID HEALTH INSURANCE COVERAGE, OR NO HEALTH INSURANCE COVERAGE FROM THE MONTH BEFORE PREGNANCY TO THE TIME OF DELIVERY, OKLAHOMA AND U.S., PREGNANCY RISK 115 ASSESSMENT MONITORING SYSTEM, 2009. Time Period/ Oklahoma United States Coverage % 95% CI % 95% CI Month Prior to Pregnancy Private Insurance 48.7% (45.0–52.4%) 59.9% (59.1–60.8%) Medicaid 15.0% (12.2–17.9%) 16.6% (16.0–17.3%) Uninsured 36.2% (32.6–39.9%) 23.4% (22.6–24.2%) During Pregnancy Private Insurance 44.2% (40.6–47.9%) 56.9% (56.1–57.8%) Medicaid 52.3% (48.6–56.0%) 40.1% (39.2–40.9%) Uninsured 3.4% (2.1–4.8%) 3.0% (2.6–3.4%) At Delivery Private Insurance 42.1% (38.5–45.8%) 54.6% (53.8–55.5%) Medicaid 55.4% (51.7–59.1%) 43.9% (43.0–44.7%) Uninsured 2.5% (1.3–3.7%) 1.5% (1.3–1.7%)

During 2013, SoonerCare (Oklahoma’s Medicaid program) covered 61.6% of the deliveries in Oklahoma. During the past decade the percentage of deliveries covered by SoonerCare has varied from a high of 63.5% in calendar year (CY) 2009 to a low of 56.0% in CY 2005. In CY 2013, the percentage of deliveries covered by SoonerCare was

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OKLAHOMA BIRTHSB AND SOONERCARE DELIVERIESA BY CALENDAR YEAR.

32,888 32,601 33,898 31,848 32,517 32,841

CY2004 CY2005 CY2006 CY2007 CY2008 CY2009 CY2010 CY2011 CY2012 CY2013

Oklahoma Births SoonerCare Deliveries

Delivery refers to a paid claim with a delivery code.

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In March 2015, the U.S. Congress introduced legislation to create HSPA, aka Health Professional Shortage Area, designation for maternity care. While the new designation has yet to be implemented, many Oklahoma women drive more than an hour to reach prenatal care.

OBSTETRICIANS AND GYNECOLOGISTS (OB/GYNS) PER 10,000 WOMEN, OKLAHOMA, 2014.

 Oklahoma, Tulsa, Cherokee, and Jackson counties had the highest rates of OB/GYNs per 10,000 women.  More than half (58%) of Oklahoma counties did not have an OB/GYN.  Many OB/GYNs travel to provide care.

A review of the chronic health factors found that Black mothers were more likely to have been checked or treated for high blood pressure and diabetes and more likely to have talked with a health care provider about a family medical history than White mothers. American Indian mothers were more likely to have been checked/treated for diabetes but less likely to have been checked or treated for depression than White mothers.

ADJUSTED* RISK RATIO TO DETERMINE RACIAL/ETHNIC DISPARITIES IN SELECTED PRE-PREGNANCY CHRONIC HEALTH FACTORS, PREGNANCY RISK ASSESSMENT MONITORING SYSTEM, OKLAHOMA 2009-2011.

Maternal Race, Adjusted Risk Ratio, (95% C.I.) Chronic Health White Black American Other Hispanic

Conditions Indian

Checked/treated for high blood pressure Ref 2.3 1.3 1.0 1.0 (1.5 - 3.7) (0.8 - 2.1) (0.5 -1.8) (0.6 - 1.6)

7/30/2015 Checked/treated for | | diabetes Ref 2.4 3.0 0.6 2.1 (1.3 - 4.3) (1.9 - 4.8) (0.3 - 1.3) (1.3 - 3.4) Checked/treated for depression Ref 0.6 0.5 0.9 0.4 (0.3 – 1.0) (0.3 - 0.8) (0.6 - 1.3) (0.2 - 0.6) Talked to provider about family medical history Ref 1.5 1.0 1.0 0.9 (1.1 - 2.0) (0.8 - 1.4) (0.7 - 1.4) (0.7 - 1.3) Taking prescription drugs Ref 0.8 0.8 0.7 0.5 (0.5 - 1.1) (0.6 - 1.2) (0.5 - 1.0) (0.3 - 0.8) *Adjusted for age, income, marital status, education, and insurance. Bolded numbers indicate significant differences compared to

others. PopulationHealth Needs Assessment

63 Some differences, such as those for smoking and alcohol use prior to pregnancy were lower or the same among the populations most at risk for infant and maternal mortality when compared to White mothers. Limitations for this study include social desirability bias for some questions, such as those relating to tobacco and alcohol use; lack of knowledge about whether the mother visited a doctor prior to pregnancy; no information on why the mothers were checked or treated and who provided the check or treatment for diabetes and blood pressure; and, racism as a stressor is not evaluated by PRAMS and may be an important contributor to the disparities and poor health outcomes in Oklahoma.

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C. Health Costs Even the best health care during pregnancy cannot overcome poor health conditions and risk factors that existed prior to conception. Infants may be low birth weight because of inadequate intrauterine growth or premature birth. Risk factors include sociodemographic and behavioral characteristics, such as low income and tobacco use during pregnancy. Proper preventive care may reduce incidence of low birth weight, and lower rates represent better quality care.

COST SAVING FROM REDUCTION IN PREVENTABLE HOSPITAL STAYS AMONG LOW BIRTH WEIGHT INFANTS, MONAHRQ®, OKLAHOMA, 2013. Low Birthweight Costs Savings by Percentage Reduction in Stays Babies

# of Stays 10% 20% 30% 40% 50% 2,899 $7,812,900 $15,625,800 $23,438,700 $31,251,600 $39,064,500

*Cost savings are based on charges that have been adjusted to costs, using hospital-specific cost-to-charge ratios.

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10. Appendices A. Comparison of Ranking Indicators

B. Oklahoma Report, America’s Health Ranking®, 2014

C. Oklahoma and U.S. Measures, County Health Rankings Data

D. County Level Risk Factors and Chronic Diseases among Persons 18 Years and Older, Oklahoma Behavioral Risk Factor Surveillance System, 2013

E. County Level Mortality by Cause and Years, Oklahoma Vital Statistics

F. Data Sources and Methods

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A. Comparison of Ranking Indicators America’s Health Ranking® County Health Rankings State Health System Performance Scorecard Adult obesity Adult obesity Adults ages 18-64 who are obese (BMI >=30) Adult smoking Adult smoking Adults who smoke Poor physical health days Poor physical health days Adults ages 18-64 who report fair/poor health or activity limitations Lack of health insurance Uninsured Adults ages 19-64 uninsured Premature death Premature death Years of potential life lost before age 75 Preventable Hospitalizations Prevent Hospital stays Hospital admissions for ambulatory care-sensitive conditions per 1,000 beneficiaries by age group Air pollution Air pollution - particulate matter Children in poverty Children in poverty Dentists Dentists Diabetes Diabetic monitoring LEGEND Excessive drinking Excessive drinking All 3 Rankings share similar indicators High school graduation High school graduation County Health Rankings and America’s Health Rankings share similar indicators

Income Disparity Income inequality America’s Health Ranking and Health System Scorecard share similar indicators

Low Birthweight Low birthweight Unique indicator to Ranking Physical Inactivity Physical inactivity Poor Mental health Days Poor Mental Health Days

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Chlamydia Sexually transmitted infections Teen Birth Rate Teen Births Unemployment Rate, Annual Unemployment Violent Crime Violent Crime Obesity youth Children ages 10-17 who are overweight or obese Infant mortality Infant mortality Suicide Suicide deaths Dental visit, annual Adults without a dental visit in past year Teeth Extractions Percent of adults ages 18-64 who have lost six or more teeth because of tooth decay, infection, or gum disease Immunizations - children Children ages 19-35 months who received all recommended doses of seven key vaccines immunizations - DTAP

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America’s Health Ranking® County Health Rankings State Health System Performance Scorecard Binge Drinking Access to exercise opportunities Adults age 50 and older who received recommended screening and preventive care Cancer Deaths Alcohol-impaired driving deaths Adults who went without care because of cost in past year Cardiovascular Deaths Children in single-parent households Adults with usual source of care Cholesterol Check Drinking water violations At-risk adults without a routine doctor visit in the past 2 years Colorectal Cancer Screening Driving alone to work Breast cancer deaths per 100,000 female population Disparity in health Status Food environment index Children 0-18 uninsured Drug Deaths Injury deaths Children with a medical and dental preventive care visit Fruits Long commute - driving alone Children with a medical home Heart Attack Mammography screening Children with emotional/behavioral/development problems who received needed mental health care Heart Disease Mental health providers Colorectal cancer deaths per 100,000 population High Blood Pressure Poor or Fair Health High-risk nursing home residents with pressure sores High Cholesterol Severe housing problems Home health patients also enrolled in Medicare with a hospital admission. High Health Status Social Associations Home health patients who get better at walking or moving around Immunizations - Adolescent Some college Home health patients whose wounds improved or healed after an operation Immunizations – HPV Hospital admissions for pediatric asthma, per 100,000 children Income Disparity Ratio Hospitalized patients given information about what to do during their recovery at home Hospitalized patients who reported hospital staff always managed pain well, responded when need help to get to Infectious Disease bathroom or pressed call button, and explained medicines and side effects.

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Insufficient Sleep Individuals under age 65 with high out-of-pocket medical costs relative to their annual household income |

Median Household Income Long-stay nursing home residents hospitalized within a six-month period. Occupational Fatalities Long-stay nursing home residents with an antipsychotic medication Personal Income, Per Capita Medicare 30-day hospital readmissions, rate per 1,000 beneficiaries Medicare beneficiaries with dementia, hip/pelvic facture, or chronic renal failure who received a prescription Pertussis drug that is contraindicated for that condition Physical Activity Medicare Beneficiaries who received at least one drug that should be avoided in the elderly Medicare fee-for-service patients whose health provider always listens, explains, shows respect, and spends Preterm Birth enough time with them Public Health Funding Mortality amenable to health care, deaths per 100,000 population Salmonella Potentially avoidable emergency department visits among Medicare Beneficiaries, per 1,000 beneficiaries Stroke Risk-adjusted 30 day mortality - Medicare beneficiaries hospitalized for heart attack, heart failure, or pneumonia

Underemployment Rate Short-stay nursing home residents readmitted within 30 days of hospital discharge to nursing home PopulationHealth Needs Assessment

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America’s Health Ranking® County Health Rankings State Health System Performance Scorecard Vegetables Total Medicare (parts a & b) reimbursements per enrollee

Youth Smoking Total single premium per enrolled employee at private-sector establishments that offer health insurance

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B. Oklahoma Report, America’s Health Rankings ® , 2014

State State # 1 Indicators Value Rank State Behaviors Smoking (Percent of adult population) 23.7 45 10.3 Binge Drinking (Percent of adult population) 12.7 7 9.6 Drug Deaths (Deaths per 100,000 population) 19.8 45 3.0 Obesity (Percent of adult population) 32.5 44 21.3 Physical Inactivity (Percent of adult population) 31.1 47 16.2 High School Graduation (Percent of incoming ninth graders) 79.0 30 93.0 Community & Environment Violent Crime (Offenses per 100,000 population) 469 41 123 Occupational Fatalities (Deaths per 100,000 workers) 7.1 44 2.2 Infectious Disease (Combined score Chlamydia, Pertussis, Salmonella*) -0.07 25 -0.9 Chlamydia (Cases per 100,000 population) 444.2 27 233.0 Pertussis (Cases per 100,000 population) 4.1 6 1.6 Salmonella (Cases per 100,000 population) 20.1 39 6.8 Children in Poverty (Percent of children) 17.8 26 9.2 Air Pollution (Micrograms of fine particles per cubic meter) 9.7 33 4.9 Policy Lack of Health Insurance (Percent of population) 18.0 44 3.8 Public Health Funding (Dollars per person) $79 24 $219 Immunization–Children (Percent aged 19 to 35 months) 62.7 47 82.1 Immunization–Adolescents (Percent aged 13 to 17 years) 59.9 39 81.3 Clinical Care Low Birthweight (Percent of live births) 8.0 24 5.7 Primary Care Physicians (Number per 100,000 population) 84.8 48 324.6 Dentists (Number per 100,000 population) 50.2 37 107.6

Preventable Hospitalizations (Number per 1,000 Medicare beneficiaries) 71.4 42 28.2 ALL DETERMINANTS -0.44 45 0.71 OUTCOMES

Diabetes (Percent of adult population) 11.0 39 6.5

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Poor Mental Health Days (Days in previous 30 days) 4.3 44 2.5 Poor Physical Health Days (Days in previous 30 days) 4.4 42 2.8 Disparity in Health Status (Percent difference by education level**) 32.1 38 15.5 Infant Mortality (Deaths per 1,000 live births) 7.4 43 4.2 Cardiovascular Deaths (Deaths per 100,000 population) 322.0 48 184.7 Cancer Deaths (Deaths per 100,000 population) 214.1 45 145.7 Premature Deaths (Years lost per 100,000 population) 9,654 46 5,345 ALL OUTCOMES -0.30 47 0.34 OVERALL -0.74 46 0.91

*Negative score denotes less than US average, positive score indicates more than US average. **Difference in the percentage of adults aged 25 and older with vs without a high school education who report their health is very good

or excellent. PopulationHealth Needs Assessment

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C. Oklahoma and U.S. Measures, County Health Rankings Data Measure Description US State State State Source/Years Median Overall Min. Max. Health Outcomes Premature death Years of Potential Life Lost Rate 7681 9121 5365 15374 NCHS-Mortality/ 2010- 2012 Poor or fair health Percent of adults that report fair or poor 17% 19% 10% 30% BRFSS/2006-2012 health Poor physical health days Average # of physically unhealthy days 3.7 4.3 2.2 7.0 BRFSS/2006-2012 reported in past 30 days Poor mental health days Average # of mentally unhealthy days 3.5 4.2 1.9 6.7 BRFSS/2006-2012 reported in past 30 days Low birthweight % of live births with low birthweight 8% 8.3% 4.8% 9.9% BRFSS/2006-2012 (<2500 grams) Health Factors Health Behaviors Adult smoking % of adults who are current smokers 21% 24% 14% 39% BRFSS/2006-2012 Adult obesity % of adults that report a BMI>=30 31% 32% 28% 38% CDC Diabetes Interactive Atlas/2011 Food environment index Index of factors that contribute to a healthy 7.0 6.7 4.5 8.2 USDA Food Environment

food environment, (0‐10) Atlas/2012

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Physical inactivity % of adults aged 20 and over reporting no 27% 30% 23% 41% CDC Diabetes Interactive leisure‐time physical activity Atlas/2011 Access to exercise % of population with adequate access to 65% 72% 0% 93% Delorme data, ESRI, opportunities locations for physical activity Census/2010 & 2013 Excessive drinking % of adults reporting binge or heavy 16% 13% 7% 21% BRFSS/2006-2012 drinking Alcohol-impaired driving % of driving deaths with alcohol 31% 33% 0% 63% FARS/2009-2013 deaths involvement Sexually transmitted # of newly diagnosed chlamydia cases per 291 442 125 776 CDC/2012 infections 100,000 population Teen births # of births per 1,000 female population ages 15‐ 41 54 22 95 NCHS Natality/2006- 19 2012

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Measure Description US State State State Source/Years Median Overall Min. Max. Clinical Care Uninsured % of population under age 65 without health 17% 21% 15% 29% SAHIE/2012 insurance Primary care physicians Ratio of population to primary care 2015:1 1567:1 14555:1 821:1 AMA/2012 physicians Dentists Ratio of population to dentists 2670:1 1805:1 12377:1 999:1 Area Health Resource File/2013 Mental health providers Ratio of population to mental health 1128:1 285:1 6432:1 101:1 CMS/2014 providers Preventable hospital # of hospital stays for ambulatory‐care 65.3 71 28 250 Dartmouth Atlas/2012 stays sensitive conditions per 1,000 Medicare enrollees Diabetic monitoring % of diabetic Medicare enrollees ages 65‐75 85% 78% 45% 89% Dartmouth Atlas/2012 that receive HbA1c monitoring Mammography % of female Medicare enrollees ages 67‐69 61% 55.3% 37.8% 66.2% Dartmouth Atlas/2012

that receive mammography screening

Social and Economic Factors High school education % of ninth‐grade cohort that graduates in 85% 78% 27% 100% Data.gov/2011-2012 four years

Some college % of adults ages 25‐44 with some post‐ 56% 58.4% 38.0 70.1 ACS/2009-2013 7/30/2015 | | secondary education Unemployment % of population aged 16 and older 7% 5.4% 2.6% 9.6% BLS/2013 unemployed but seeking work Children in poverty % of children under age 18 in poverty 24% 24% 11% 41% SAIPE/2013 Income inequity Ratio of household income at the 80th 4.4 4.6 3.4 6.2 ACS/2009-2013 percentile to income at the 20th percentile Children in single-parent % of children that live in a household headed 31% 34% 17% 49% ACS/2009-2013 households by single parent Social associations # of membership associations per 10,000 12.6 11.8 5.5 29.4 Community Business population Patterns/2012 Violent crime # of reported violent crime offenses per 199 468 18 991 Uniform Crime 100,000 population Reporting/2010-2012

Injury deaths # of deaths due to injury per 100,000 73.8 86 60 158 CDC WONDER/2008-12 PopulationHealth Needs Assessment

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Measure Description US State State State Source/Years Median Overall Min. Max. Physical Environment Air pollution- particulate Average daily density of fine particulate 11.9 10.3 9.3 12.0 CDC WONDER/ 2011 matter matter in micrograms per cubic meter (PM2.5) Drinking water % of population potentially exposed to 1.0% 23% 0% 96% Safe Drinking Water violations water exceeding a violation limit during the Information past year System/FY2013-2014 Severe housing problems % of households with overcrowding, high 14% 14% 5% 22% CHAS/2007-2011 housing costs, or lack of kitchen or plumbing facilities Driving alone to work % of workforce that drives alone to work 80% 82% 72% 88% ACS/2009-2013 Long-commute- driving Among workers who commute in their car 29% 25% 9% 49% ACS/2009-2013 alone alone, % commuting > 30 minutes

ACS=American Community Survey; AMA=American Medical Association; BLS=Bureau of Labor Statistics; BRFSS=Behavioral Risk Factor Surveillance System, CDC= Centers for Disease Control and Prevention; CHAS=Comprehensive Housing Affordability Strategy data; CMS=Centers for Medicare and Medicaid Services; FARS = Fatality Analysis Reporting System; SAHIE = U.S. Census, Small Area Health Insurance Estimates; SAIPE=Small Area Income and Poverty

Estimates; NCHS=National Center for Health Statistics; USDA= United States Department of Agriculture. 7/30/2015

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D. County Level Risk Factors and Chronic Diseases among Persons 18 Years and Older, Oklahoma Behavioral Risk Factor Surveillance System, 2013 County of % % % No leisure % At least 1 fruit % At least 1 % Told had % Told had residence Smokers Obesity time activity per day vegetable per day diabetes hypertension

Adair 36.0% 44.4% 44.4% 65.7% 32.5% 16.6% 35.5% Alfalfa 37.8% 35.4% 43.9% 55.3% 23.3% 11.9% 40.8% Atoka 35.0% 36.3% 34.4% 52.1% 27.4% 9.8% 38.7% Beaver 20.9% 31.0% 34.4% 52.1% 30.4% 16.6% 40.2% Beckham 25.4% 30.5% 36.3% 55.6% 29.8% 6.7% 38.2% Blaine 19.9% 35.4% 39.9% 46.6% 15.8% 13.6% 42.1% Bryan 21.6% 33.7% 33.7% 60.3% 35.7% 9.6% 50.3% Caddo 29.7% 41.4% 42.8% 52.1% 27.2% 10.6% 37.8%

Canadian 20.2% 32.3% 35.3% 49.7% 24.7% 10.8% 35.6% Carter 31.3% 35.8% 30.7% 46.6% 26.3% 10.4% 39.3%

Cherokee 22.8% 30.3% 25.6% 52.5% 28.2% 13.9% 39.3%

7/30/2015 | | Choctaw 27.5% 32.0% 38.2% 51.7% 27.6% 17.7% 42.2% Cimarron 25.6% 25.6% 39.6% 59.4% 17.4% 10.8% 45.1% Cleveland 17.6% 30.3% 26.1% 47.3% 24.9% 8.9% 34.8% Coal 28.7% 38.4% 45.4% 51.8% 14.9% 8.9% 37.4% Comanche 25.5% 32.6% 30.3% 53.4% 27.6% 10.2% 35.9% Cotton 32.8% 37.4% 42.0% 53.0% 26.3% 14.9% 43.1% Craig 23.5% 27.7% 33.1% 62.5% 29.4% 12.7% 48.5%

Creek 35.5% 39.0% 36.1% 51.3% 21.0% 13.2% 46.3% PopulationHealth Needs Assessment

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County of % % % No leisure % At least 1 fruit % At least 1 % Told had % Told had residence Smokers Obesity time activity per day vegetable per day diabetes hypertension

Custer 25.4% 29.9% 29.6% 53.6% 26.2% 9.7% 35.3% Delaware 29.7% 34.8% 35.8% 52.6% 25.6% 16.5% 42.1% Dewey 24.4% 32.9% 40.8% 52.7% 29.1% 12.6% 39.2% Ellis 27.0% 33.7% 38.6% 57.0% 21.0% 9.4% 39.5% Garfield 25.5% 35.4% 40.3% 47.3% 21.8% 9.4% 31.2% Garvin 23.8% 32.8% 41.2% 58.5% 25.2 12.3% 42.4% Grady 28.0% 32.5% 29.1% 59.4% 23.2% 10.8% 35.6% Grant 18.5% 35.8% 40.3% 61.2% 29.6% 9.6% 45.5% Greer 43.0% 32.0% 40.5% 58.6% 17.0% 11.6% 51.3%

Harmon 25.8% 38.5% 43.9% 49.5% 28.1% 13.5% 47.0%

Harper 20.0% 35.3% 39.0% 64.8% 42.3% 11.6% 42.9%

Haskell 22.3% 33.8% 42.9% 45.3% 30.0% 19.5% 44.9% 7/30/2015

| | Hughes 33.7% 34.6% 49.7% 59.2% 45.0% 16.8% 44.7%

Jackson 20.3% 25.2% 40.1% 54.5% 25.6% 10.7% 29.0% Jefferson 37.0% 33.4% 41.0% 45.3% 23.0% 8.8% 51.8% Johnston 29.7% 44.8% 38.7% 52.9% 27.3% 10.0% 48.9% Kay 21.9% 25.1% 31.5% 55.0% 30.0% 11.9% 38.1% Kingfisher 17.8% 24.6% 30.9% 49.2% 29.1% 9.9% 30.8% Kiowa 25.3% 27.2% 43.4% 48.6% 16.0% 15.3% 45.7%

Latimer 23.6% 32.3% 29.9% 48.0% 31.8% 14.5% 45.5% PopulationHealth Needs Assessment

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County of % % % No leisure % At least 1 fruit % At least 1 % Told had % Told had residence Smokers Obesity time activity per day vegetable per day diabetes hypertension

Leflore 26.6% 42.9% 33.6% 50.7% 22.8% 16.8% 41.0% Lincoln 29.0% 40.5% 41.0% 54.7% 15.8% 10.8% 38.9% Logan 20.0% 28.9% 31.5% 45.0% 19.1% 12.5% 38.0% Love 30.3% 36.4% 35.2% 56.0% 23.1% 19.1% 45.1% McClain 21.8% 32.5% 25.6% 51.0% 29.9% 10.2% 40.3% McCurtain 27.8% 36.6% 40.4% 58.6% 33.1% 11.8% 38.1% McIntosh 29.4% 36.8% 44.1% 51.2% 32.2% 13.4% 52.7% Major 22.4% 33.4% 33.7% 49.0% 19.5% 9.6% 44.3% Marshall 22.5% 37.7% 48.6% 49.1% 25.5% 11.6% 50.5%

Mayes 28.4% 37.4% 41.1% 57.7% 25.0% 13.2% 41.3%

Murray 33.5% 37.0% 35.9% 52.3% 29.0% 9.5% 30.3%

Muskogee 33.9% 33.9% 31.7% 49.0% 25.9% 11.7% 40.7% 7/30/2015

Noble 15.6% 35.0% 36.8% 55.9% 19.6% 14.5% 43.5% |

Nowata 18.4% 34.5% 32.4% 54.6% 23.7% 11.7% 39.8% Okfuskee 33.8% 35.8% 30.1% 56.8% 26.3% 10.0% 48.6% Oklahoma 22.3% 31.0% 32.8% 48.6% 25.2% 10.2% 34.5% Okmulgee 27.8% 40.2% 38.9% 45.9% 24.0% 9.2% 43.2% Osage 27.9% 34.9% 33.5% 56.3% 27.9% 10.6% 42.6% Ottawa 31.1% 33.6% 32.8% 62.1% 27.3% 13.3% 38.8%

Pawnee 22.4% 35.2% 34.7% 58.1% 31.2% 13.1% 44.8% PopulationHealth Needs Assessment

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County of % % % No leisure % At least 1 fruit % At least 1 % Told had % Told had residence Smokers Obesity time activity per day vegetable per day diabetes hypertension

Payne 16.5% 26.4% 22.4% 45.3% 26.4% 5.4% 29.2% Pittsburg 29.3% 28.0% 37.1% 57.0% 29.6% 12.5% 40.3% Pontotoc 25.0% 34.0% 32.2% 43.4% 25.5% 9.7% 42.1% Pottawatomie 26.6% 38.1% 29.5% 48.5% 25.6% 13.9% 38.3% Pushmataha 33.4% 27.7% 38.0% 58.1% 25.2% 20.4% 64.3% Roger Mills 19.8% 28.1% 42.9% 56.2% 18.2% 18.6% 43.6% Rogers 17.7% 32.4% 31.5% 49.3% 24.3% 9.8% 32.2% Seminole 24.5% 38.8% 38.7% 58.1% 35.5% 12.6% 39.1% Sequoyah 30.9% 42.8% 39.1% 61.9% 29.8% 17.8% 48.6%

Stephens 26.9% 33.8% 39.3% 48.4% 20.8% 14.4% 37.3%

Texas 20.2% 28.5% 37.9% 43.3% 12.6% 7.4% 29.6%

Tillman 25.7% 28.1% 40.9% 62.0% 40.8% 11.6% 38.7% 7/30/2015

| | Tulsa 20.9% 30.0% 30.9% 47.9% 22.9% 10.3% 35.3%

Wagoner 23.4% 36.8% 33.1% 46.9% 23.9% 12.5% 43.3% Washington 23.7% 29.5% 31.3% 49.1% 26.3% 14.2% 46.2% Washita 27.4% 38.1% 34.0% 54.5% 32.2% 12.4% 40.8% Woods 24.1% 31.1% 40.9% 47.3% 24.9% 11.8% 31.9% Woodward 27.9% 38.0% 33.0% 47.4% 21.8% 9.0% 38.0%

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E. County Level Mortality by Cause and Years, Oklahoma Vital Statistics County of Suicide, Infant deaths per 1,000 Unintentional injury, Diabetes, age-adjusted rate residence age- live births, 2009 -2013 age-adjusted rate per per 100,000 population, 2011 adjusted 100,000 population less - 2013 rate per than 18 years, 2009-2013 100,000 population, 2011-2013

Adair * 12.6 * 67.7 Alfalfa * * * 29.5 Atoka * 7.3 * 36.1 Beaver * * 0 32.7 Beckham 36.6 9.9 * 40.3

Blaine * 15.3 39.8 43.1

Bryan 17.3 5.1 17.9 29.6 Caddo 19.4 9.9 24 49.3

Canadian 17.6 5.4 8.1 28 7/30/2015

| |

Carter 21.7 5.8 21.1 35.1 Cherokee 23.4 7.3 17.7 36.6 Choctaw 15.5 7.6 * 22.1 Cimarron 0 0 * 40.5 Cleveland 12.9 5 9.1 19.1 Coal * * * *

Comanche 18.1 9.1 7.1 41.6 PopulationHealth Needs Assessment

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County of Suicide, Infant deaths per 1,000 Unintentional injury, Diabetes, age-adjusted rate residence age- live births, 2009 -2013 age-adjusted rate per per 100,000 population, 2011 adjusted 100,000 population less - 2013 rate per than 18 years, 2009-2013 100,000 population, 2011-2013

Cotton * * * 34 Craig 20.8 6.9 * 23.3 Creek 21.1 8.8 17.3 29.5 Custer 14.6 6.2 21.3 36.9 Delaware 22.7 6.7 * 24.8 Dewey * * * 34.1

Ellis * 0 * 24.6 Garfield 22.2 7.6 * 32.2

Garvin 25.2 6.9 26.7 39.2

7/30/2015 | | Grady 16.1 5.8 21.3 42.4 Grant * 19.3 * 73.5 Greer * * * 16 Harmon 0 * 0 * Harper * * * * Haskell 17.6 8.3 * 33.3 Hughes 16.2 6.7 46.1 22.9

Jackson 14.2 9 23.4 32.2 PopulationHealth Needs Assessment

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County of Suicide, Infant deaths per 1,000 Unintentional injury, Diabetes, age-adjusted rate residence age- live births, 2009 -2013 age-adjusted rate per per 100,000 population, 2011 adjusted 100,000 population less - 2013 rate per than 18 years, 2009-2013 100,000 population, 2011-2013

Jefferson 28.3 * 77.8 31.3 Johnston 41.7 8.6 * 33.7 Kay 14.4 7.9 * 32.5 Kingfisher 10 * * 28.3 Kiowa * 10.9 * 41.3 Latimer * 0 * 33.9

Leflore 12.7 4.1 29.3 40.3 Lincoln 19.9 8.1 14 20.5

Logan 11.8 6.9 * 25.4

7/30/2015 | | Love * * * 35.1 McClain 14.5 10.7 15 23.2 McCurtain 25.4 9.9 20.9 52.3 McIntosh 15 13.4 * 28.3 Major * 18.8 0 31.5 Marshall 19.6 7.3 31.6 18.7 Mayes 18.7 7 13.4 36.5

Murray 25.4 12.5 * 31 PopulationHealth Needs Assessment

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County of Suicide, Infant deaths per 1,000 Unintentional injury, Diabetes, age-adjusted rate residence age- live births, 2009 -2013 age-adjusted rate per per 100,000 population, 2011 adjusted 100,000 population less - 2013 rate per than 18 years, 2009-2013 100,000 population, 2011-2013

Muskogee 15.4 6.1 11.5 28.8 Noble * 11.4 0 47.1 Nowata 11.5 8.6 * 15.4 Okfuskee 21.4 9.3 48.5 56.5 Oklahoma 17.7 8 9.9 27.7 Okmulgee 21.3 7.8 24.7 41.4

Osage 20.1 8 8.8 27.2 Ottawa 23.5 10.3 22.8 26.6

Pawnee 21.1 5.2 39.5 37.1

7/30/2015 | | Payne 11.7 4.8 12.3 25.1 Pittsburg 28.3 5.5 17.9 27.2 Pontotoc 7.6 6.5 18 28.4 Pottawatomie 22 8.7 21.8 31.6 Pushmataha 30.2 7.5 * 29.5 Roger Mills * * 0 * Rogers 19.1 7.4 12.5 24.6

Seminole 21 7.7 * 35.9 PopulationHealth Needs Assessment

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County of Suicide, Infant deaths per 1,000 Unintentional injury, Diabetes, age-adjusted rate residence age- live births, 2009 -2013 age-adjusted rate per per 100,000 population, 2011 adjusted 100,000 population less - 2013 rate per than 18 years, 2009-2013 100,000 population, 2011-2013

Sequoyah 19.7 5.6 11.3 36.9 Stephens 24.3 8.4 9.3 21.3 Texas * 9.6 23.2 38.2 Tillman * 9.7 0 56.5 Tulsa 18.7 7.2 10.8 24.9 Wagoner 17.4 6.3 12.4 29.4

Washington 17.3 6.7 13.3 17.1 Washita 16.1 8.3 46.8 32.2

Woods * * 0 35.6

7/30/2015 | | Woodward 8.9 8.4 * 36.1

Note:* = When there are too few cases (<5), data are not shown to protect confidentiality. Colored fields indicate unstable rates, meaning that the rates drastically vary year to year due to the small number of cases (< 20). Age-adjusted rates are used to control for differences in age distribution of the populations between counties so that the county rates are comparable.

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F. Data Sources and Methods METRICS DATA SOURCES

America’s Health United Health Foundation, America’s Health Rankings, 2014. Retrieved from Ranking® www.americashealthrankings.org/. Commonwealth Fund The Commonwealth Fund, Health System Performance Scorecard, 2014. Retrieved State Health System from http://datacenter.commonwealthfund.org/#ind=1/sc=1 Performance Scorecard Oklahoma State Department of Health (OSDH), Center for Health Statistics, Health Care Information, Behavioral Risk Factor Surveillance System (BRFSS) 2013, on Oklahoma Statistics on Health Available for Everyone (OK2SHARE). Retrieved from Adult Smoking Prevalence www.health.ok.gov/ok2share. Centers for Disease Control and Prevention (CDC), BRFSS Prevalence and Trends Data, 2013. Retrieved from www.cdc.gov/brfss/brfssprevalence/. Youth Smoking OSDH, Center for the Advancement of Wellness, Youth Tobacco Survey 2013. Prevalence OSDH, Center for Health Statistics, Health Care Information, BRFSS 2013, on OK2SHARE. Retrieved from www.health.ok.gov/ok2share. CDC, BRFSS Adult Obesity Prevalence and Trends Data, 2013. Retrieved from www.cdc.gov/brfss/brfssprevalence/. OSDH, Maternal and Child Health Service (MCH), MCH Assessment, Youth Risk Youth Obesity Behavior Surveillance System 2013, on OK2SHARE. Retrieved from www.health.ok.gov/ok2share. OSDH, Center for Health Statistics, Health Care Information, BRFSS 2013, on Adult No Leisure Time OK2SHARE. Retrieved from www.health.ok.gov/ok2share. CDC, BRFSS Activity Prevalence and Trends Data, 2013. Retrieved from www.cdc.gov/brfss/brfssprevalence/. OSDH, Center for Health Statistics, Health Care Information, BRFSS 2013. CDC, Adult Fruit Consumption BRFSS Prevalence and Trends Data, 2013. Retrieved from www.cdc.gov/brfss/brfssprevalence/. Adult Vegetable OSDH, Center for Health Statistics, Health Care Information, BRFSS 2013. Consumption Food Desert/Food United States Department of Agriculture (USDA), Economic Research Service, Food Research Atlas. Retrieved from www.ers.usda.gov/data-products/food-access- Availability research-atlas/go-to-the-atlas.aspx. OSDH, Center for Health Statistics, Health Care Information, BRFSS 2013. CDC Adult Diabetes BRFSS Prevalence and Trends Data, 2013. Retrieved from www.cdc.gov/brfss/brfssprevalence/.

7/30/2015 OSDH, Center for Health Statistics, Health Care Information, BRFSS 2013. CDC, | | Adult Hypertension BRFSS Prevalence and Trends Data, 2013. Retrieved from www.cdc.gov/brfss/brfssprevalence/. Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. (September 4, 2014). The NSDUH Report: Substance Use Untreated Mental Illness and Mental Health Estimates from the 2013 National Survey on Drug Use and Health: Any Mental Illness in the Past Year – Table 24, Rockville, MD. Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. (September 4, 2014). The NSDUH Report: Substance Use Addiction Disorders and Mental Health Estimates from the 2013 National Survey on Drug Use and Health: Dependence or Abuse of Illicit Drugs or Alcohol in the Past Year - Table 20, Rockville, MD. Suicide Deaths OSDH, Center for Health Statistics, Health Care Information, Vital Statistics

18 years and older 2013. CDC Web-based Injury Statistics Query and Reporting System PopulationHealth Needs Assessment

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METRICS DATA SOURCES

(WISQARS), Mortality, 2013. Retrieved from http://wonder.cdc.gov/. Vital Statistics 2013. CDC Wide-ranging Online Data for Epidemiological Infant Mortality Research (WONDER, Infant Deaths, 2013. Retrieved from http://wonder.cdc.gov/. Maternal Mortality Vital Statistics 2013. CDC WONDER, Mortality, 2013. Retrieved from All ages, 1 yr. rate http://wonder.cdc.gov/. OSDH, Center for Health Statistics, Health Care Information, Vital Statistics 2013. Injury Deaths Among 0-17 CDC WISQARS, Mortality, 2013. Retrieved from years http://wonder.cdc.gov/.

Limitations Mortality-specific data concerns:  Age There is a worsening trend related to advancing age given the natural risk of dying as age increases.  Race/Mortality Race is not self-reported on Death Certificates, and as such is subject to racial misclassification. Oklahoma linkage studies with Indian Health Services indicate one-third of American Indian deaths in Oklahoma are classified as white. Consequently, often American Indian mortality rates are based on numerators that have been undercounted. Certain causes of death that typically are included in American Indian studies, such as diabetes, tend to have more accurate coding, but will still be under represented. Hispanics Death Rates. There may be a cultural effect resulting in uncharacteristically low cause of death rates. This may be due to the immigrant population returning to their country of birth prior to death. This will underestimate the overall rate of death generally, but particularly among that migrant population group.  County-level infant mortality rates National and state-level mortality data were taken from the Centers for Disease Control and Prevention (CDC) WONDER web-based data query system and reflect the five year period from 2009-2013.

 Deaths from heart disease Heart disease-related deaths were identified by ICD-10 codes: 100-109, 111, 113, and 120-151.

Data sources and related issues:

Sources for a number of health indicators were the Behavioral Risk Factor Surveillance System, Youth Risk Behavior 7/30/2015 | | Survey, and Youth Tobacco Survey. Survey data are subject to sampling error. As a result, responses obtained from the selected sample may differ from the targeted population from which the sample is drawn. Margin of error in sample estimates exist and may impact the distribution of survey responses. Year-on-year differences may also occur. Rather than representing real changes in the population, yearly fluctuation may indicate sampling error. Confidence intervals calculated at 95% are used to account for the variation in survey methodology. Meaning, 95 times out of a 100 samples, the percentage will be within the range specified. If ranges of the same variable overlap, or contain the same percentage, then the percentages are not statistically different.

The maps were generated using Behavioral Risk Factor Surveillance System data that represents modeled estimates for all of the counties.

Age-adjusted rates using the 2000 US Standard Population were reported (exception: infant mortality). PopulationHealth Needs Assessment

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The source of maternal health data was the Pregnancy Risk Assessment Monitoring System (PRAMS). PRAMS is an ongoing, population-based study designed to collect information about maternal behaviors and experiences before, during, and after pregnancy. Monthly, PRAMS sampled between 200 to 250 recent mothers taken from the Oklahoma live birth registry. Mothers were mailed up to three questionnaires seeking their participation. Follow-up phone interviews for non-respondents were conducted. A systematic stratified sampling design was used to yield sample sizes sufficient to generate population estimates for groups considered at risk for adverse pregnancy outcomes. Information included in the birth registry is used to develop analysis weights that adjust for probability of selection and non-response.

When few events occur in a given county or among a demographic group, the resulting rate is considered unstable or unreliable due to its large relative standard error. This is also the case when making estimates about the population using sample sizes smaller than 50 (as is the case with the BRFSS data). Thus, data for each indicator may not be available for every demographic and county.

The tables that show the number and rate of preventable hospitalizations for the health condition and the cost savings associated with preventing a percentage of the hospitalizations by county or state were limited. Counties with 10 or fewer discharges are not included in the tables to protect the confidentiality of patients. The data system excludes hospitalizations:  Transferred from another hospital,  Transferred from a skilled nursing facility or intermediate care facility,  Transferred from another health care facility in another State, and  Hospitalizations in Tribal Nation, Indian Health Service, or Veterans facilities.

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11. Endnotes

1. U.S. Census Bureau. (2014).Tiger line files (data file). Retrieved from http://www.census.gov/. 2. Selinsky’s Cultural Identity Map. Web Atlas of Oklahoma. Ada, Oklahoma: East Central University. Retrieved from http://www.okatlas.org/ 3. Newschaffer CJ, Liu L, & Sim A. (2010). Cardiovascular disease. Chronic disease epidemiology and control, 2010:383. 4. American Cancer Society. Learn About Cancer. Retrieved from http://www.cancer.org/index 5. U.S. Census Bureau. (2014). Vintage 2014 Population Estimates: US, State, and PR Total Population and Components of Change (data file). Retrieved from http://www.census.gov/ 6. U.S. Census Bureau. (2013). American Community Survey, 1-Year Estimates 2013 (data file). Retrieved from http://www.census.gov/ 7. U.S. Bureau of the Census (2013). Population Estimates Program (PEP), 2013 and 2005 Interim State Population Projections, Table 5 (data file). Retrieved from http://www.census.gov/ 8. United Health Foundation. (2014). America’s health rankings senior report 2014. Retrieved from http://www.americashealthrankings.org/ 9. U.S. Census Bureau. (2014). Census of Population and Housing, Tiger line files estimates (data file). Retrieved from http://www.census.gov/ 10. U.S. Census Bureau. (2014). 2010 Census Summary File 1, Table P2 - Urban and Rural Universe: Total Population (data file). Retrieved from http://www.census.gov/ 11. U.S. Census Bureau. (2010). 2010 Decennial Census (data file). Retrieved from http://www.census.gov/ 12. Oklahoma Department of Commerce. (2012). Demographic State of the State Report 2012. Retrieved from http://okcommerce.gov/data-and-research/ 13 Milliman Associates. (2014).The State of Oklahoma Business Health and Wellness Survey. Retrieved from http://www.ok.gov/health2/documents/OOC- OSDH%20Business%20Health%20and%20Wellness%20Survey%20Report%202014.pdf 14. U.S. Census Bureau. (2013). American Community Survey 5-Year Estimates, 2009-2013. Retrieved from http://www.census.gov/acs/ 15. U.S. Census Bureau. (2013). American Community Survey 1-Yr Estimates (data file). Retrieved from http://www.census.gov/acs/ 16. Feeding American. (n.d.). Map the Meal Gap (graph illustration). Retrieved from www.feedingamerica.org/hunger-in-america/our-research/map-the-meal-gap 17. The Oklahoma Academy. (2014). We can do better: Improving the health of the Oklahoma people. Retrieved from www.okacademy.org/PDFs/2014-Health.pdf 18. Rotberg B, Nelson M. (2015). Emory Diabetes Education Training Academy and Emory Latino Diabetes Education Program. Columbia University, New York City: June 9, 2015, presentation, American Diabetes Association annual meeting, Boston 19. Centers for Disease Control and Prevention. (2013). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2013. Retrieved from http://nccd.cdc.gov/brfssprevalence

20. U.S. Census Bureau. (2013). American Community Survey 5-Year Profiles, DP02-Selected Social 7/30/2015 | | Characteristics, 2009-2013 (data files). Retrieved from http://www.census.gov/acs/ 21. Kutner M, Greenberg E, Jin Y, Paulsen C. (2006). The Health Literacy of America’s Adults: Results From the 2003 National Assessment of Adult Literacy (NCES 2006–483). U.S .Department of Education. Washington, DC: National Center for Education Statistics 22. Scott TL, Gazmararian JA, Williams MV, Baker DW. (2002). Health literacy and preventive health care use among Medicare enrollees in a managed care organization. Medical Care. 40(5): 395-404 23. Bennet CL, Ferreira MR, Davis TC, Kaplan J, Weinberger M, Kuzel T, Seday MA, Sartor O. (1998). Relation between literacy, race, and stage of presentation among low-income patients with prostate cancer. Journal of Clinical Oncology. 16(9): 3101-3104 24. Baker DW, Gazmararian JA, Williams MV, et al. (2002). Functional health literacy and the risk of hospital admission among Medicare managed care enrollees. American Journal of Public Health. 92(8): 1278-1283 25. National Center for Education Statistics, Institute of Education Sciences, National Assessment of Adult Literacy, State and County Estimates of Low Literacy (1993, 2002). Retrieved from https://nces.ed.gov/naal/estimates/index.aspx PopulationHealth Needs Assessment 86

26. Taggart J, Williams A, Dennis S, et al. (2012). A systematic review of interventions in primary care to improve health literacy for chronic disease behavior risk factors. BMC Family Practice 2012, 13:49 27. U.S. Census Bureau. (2013). American Community Survey 5-Year Profiles, DP02-Selected Social Characteristics, 2009-2013 (data files). Retrieved from http://www.census.gov/acs/ 28. Braveman P, Dekker M, et al. (2011). Issue Brief 7, Exploring the Social Determinants of Health, Housing and Health. May 2011. Robert Woods Johnson Foundation 29. U.S. Census Bureau. (2013). American Community Survey 1-Yr Estimates (data file). Retrieved from http://www.census.gov/acs 30. Washington State Health Department (2015). The BRFSS Social Context Module: Using Social Context Data to Drive Chronic Disease Programs. National Association of Chronic Disease Directors Series Webinar, June 11, 2015. 31. University of Wisconsin Population Health Institute. (2015). County Health Rankings 2015. Retrieved from www.countyhealthrankings.org 32. Bauer UE, Briss PA, Goodman RA, Bowman BA. (2014). Prevention of chronic disease in the 21st Century: elimination of the leading preventable causes of premature death and disability in the USA. The Lancet, 384(9937): 45-52 33. University of Wisconsin Population Health Institute. (2015). County Health Rankings 2015. Retrieved from www.countyhealthrankings.org 34. Agency for Healthcare Research and Quality (AHRQ). (2008). National Healthcare Disparities Report 2008, Chapter 3, Access to Healthcare. Washington: AHRQ. Retrieved from www.ahrq.gov/qual/nhdr08/chap3.htm 35. Hadley J. (2007). Insurance coverage, medical care use, and short-term health changes following an unintentional injury or the onset of a chronic condition. JAMA. 2007;297(10):1073-84 36. Smith J.C. & Medalia C. (2014). Insurance health coverage in U.S.: 2013 current population reports. Washington, DC: U.S. Government Printing Office. Retrieved from http://www.census.gov/content/dam/Census/library/publications/2014/demo/p60-250.pdf 37. U.S. Census Bureau. (2013). Small Area Health Insurance Estimates (SAHIE), 2011-2013. Retrieved from www.census.gov/did/www/sahie/index.htm 38. Schoen C, Hayes S, Collins S, et al. (2014). America's Underinsured: A State-by-State Look at Health Insurance Affordability Prior to the New Coverage Expansions, The Commonwealth Fund, March 2014. Retrieved from http://www.commonwealthfund.org/Publications/Fund-Reports/2014/Mar/Americas- Underinsured.aspx. 39 US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. (2014). 2014 Poverty Guidelines. Retrieved from http://aspe.hhs.gov/poverty/14poverty.cfm 40. Oklahoma State Department of Health. (2014). 2014 State of the state’s health report. Retrieved from http://www.ok.gov/health/pub/boh/state/SOSH%202014.pdf 41. United Health Foundation. (2014). America’s health rankings. Retrieved from http://cdnfiles.americashealthrankings.org/SiteFiles/Reports/Americas%20Health%20Rankings%202014%2 0Edition.pdf 42. United Health Foundation. (2014). America’s Health Rankings 2014. Retrieved from

7/30/2015 http://www.americashealthrankings.org/reports/annual | | 43. Oklahoma Health Improvement Plan Workforce Data Committee & Oklahoma State University Center for Rural Health. (2013). Oklahoma healthcare workforce data book 2013. Retrieved from www.healthsciences.okstate.edu/ruralhealth/documents/OHIP%20Workforce%20Data%20Book.pdf 44. Oklahoma State University Center for Rural Health. (2008). State of the state’s rural health: Physicians and hospitals. Retrieved from www.healthsciences.okstate.edu/ruralhealth/docs/SOSRH%20- %202008%20Edition.pdf 45. U.S. Department of Health and Human Services. (2015). HRSA Data warehouse, Office of Primary Care, 2012 NAVTEQ. Retrieved from http://datawarehouse.hrsa.gov/ 46. Office of Primary Care and Rural Health Development. (2015). 2014-2015 Oklahoma Health Workforce Data Book. Oklahoma State Department of Health, Oklahoma City, Oklahoma 47. US Department of Health and Human Services. (2015). Designated Health Professional Shortage Areas Statistics. Bureau of Clinician Recruitment and Service, Health Resources and Services Administration. 48. U.S. Department of Health and Human Services. (2015). HRSA Data warehouse, Office of Primary Care,

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